empirical literature review of

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Module 2 Chapter 3: What is Empirical Literature & Where can it be Found?

In Module 1, you read about the problem of pseudoscience. Here, we revisit the issue in addressing how to locate and assess scientific or empirical literature . In this chapter you will read about:

• distinguishing between what IS and IS NOT empirical literature
• how and where to locate empirical literature for understanding diverse populations, social work problems, and social phenomena.

Probably the most important take-home lesson from this chapter is that one source is not sufficient to being well-informed on a topic. It is important to locate multiple sources of information and to critically appraise the points of convergence and divergence in the information acquired from different sources. This is especially true in emerging and poorly understood topics, as well as in answering complex questions.

What Is Empirical Literature

Social workers often need to locate valid, reliable information concerning the dimensions of a population group or subgroup, a social work problem, or social phenomenon. They might also seek information about the way specific problems or resources are distributed among the populations encountered in professional practice. Or, social workers might be interested in finding out about the way that certain people experience an event or phenomenon. Empirical literature resources may provide answers to many of these types of social work questions. In addition, resources containing data regarding social indicators may also prove helpful. Social indicators are the “facts and figures” statistics that describe the social, economic, and psychological factors that have an impact on the well-being of a community or other population group.The United Nations (UN) and the World Health Organization (WHO) are examples of organizations that monitor social indicators at a global level: dimensions of population trends (size, composition, growth/loss), health status (physical, mental, behavioral, life expectancy, maternal and infant mortality, fertility/child-bearing, and diseases like HIV/AIDS), housing and quality of sanitation (water supply, waste disposal), education and literacy, and work/income/unemployment/economics, for example.

Three characteristics stand out in empirical literature compared to other types of information available on a topic of interest: systematic observation and methodology, objectivity, and transparency/replicability/reproducibility. Let’s look a little more closely at these three features.

Systematic Observation and Methodology. The hallmark of empiricism is “repeated or reinforced observation of the facts or phenomena” (Holosko, 2006, p. 6). In empirical literature, established research methodologies and procedures are systematically applied to answer the questions of interest.

Objectivity. Gathering “facts,” whatever they may be, drives the search for empirical evidence (Holosko, 2006). Authors of empirical literature are expected to report the facts as observed, whether or not these facts support the investigators’ original hypotheses. Research integrity demands that the information be provided in an objective manner, reducing sources of investigator bias to the greatest possible extent.

Transparency and Replicability/Reproducibility.   Empirical literature is reported in such a manner that other investigators understand precisely what was done and what was found in a particular research study—to the extent that they could replicate the study to determine whether the findings are reproduced when repeated. The outcomes of an original and replication study may differ, but a reader could easily interpret the methods and procedures leading to each study’s findings.

What is NOT Empirical Literature

By now, it is probably obvious to you that literature based on “evidence” that is not developed in a systematic, objective, transparent manner is not empirical literature. On one hand, non-empirical types of professional literature may have great significance to social workers. For example, social work scholars may produce articles that are clearly identified as describing a new intervention or program without evaluative evidence, critiquing a policy or practice, or offering a tentative, untested theory about a phenomenon. These resources are useful in educating ourselves about possible issues or concerns. But, even if they are informed by evidence, they are not empirical literature. Here is a list of several sources of information that do not meet the standard of being called empirical literature:

• your course instructor’s lectures
• political statements
• advertisements
• newspapers & magazines (journalism)
• television news reports & analyses (journalism)
• many websites, Facebook postings, Twitter tweets, and blog postings
• the introductory literature review in an empirical article

You may be surprised to see the last two included in this list. Like the other sources of information listed, these sources also might lead you to look for evidence. But, they are not themselves sources of evidence. They may summarize existing evidence, but in the process of summarizing (like your instructor’s lectures), information is transformed, modified, reduced, condensed, and otherwise manipulated in such a manner that you may not see the entire, objective story. These are called secondary sources, as opposed to the original, primary source of evidence. In relying solely on secondary sources, you sacrifice your own critical appraisal and thinking about the original work—you are “buying” someone else’s interpretation and opinion about the original work, rather than developing your own interpretation and opinion. What if they got it wrong? How would you know if you did not examine the primary source for yourself? Consider the following as an example of “getting it wrong” being perpetuated.

Example: Bullying and School Shootings . One result of the heavily publicized April 1999 school shooting incident at Columbine High School (Colorado), was a heavy emphasis placed on bullying as a causal factor in these incidents (Mears, Moon, & Thielo, 2017), “creating a powerful master narrative about school shootings” (Raitanen, Sandberg, & Oksanen, 2017, p. 3). Naturally, with an identified cause, a great deal of effort was devoted to anti-bullying campaigns and interventions for enhancing resilience among youth who experience bullying.  However important these strategies might be for promoting positive mental health, preventing poor mental health, and possibly preventing suicide among school-aged children and youth, it is a mistaken belief that this can prevent school shootings (Mears, Moon, & Thielo, 2017). Many times the accounts of the perpetrators having been bullied come from potentially inaccurate third-party accounts, rather than the perpetrators themselves; bullying was not involved in all instances of school shooting; a perpetrator’s perception of being bullied/persecuted are not necessarily accurate; many who experience severe bullying do not perpetrate these incidents; bullies are the least targeted shooting victims; perpetrators of the shooting incidents were often bullying others; and, bullying is only one of many important factors associated with perpetrating such an incident (Ioannou, Hammond, & Simpson, 2015; Mears, Moon, & Thielo, 2017; Newman &Fox, 2009; Raitanen, Sandberg, & Oksanen, 2017). While mass media reports deliver bullying as a means of explaining the inexplicable, the reality is not so simple: “The connection between bullying and school shootings is elusive” (Langman, 2014), and “the relationship between bullying and school shooting is, at best, tenuous” (Mears, Moon, & Thielo, 2017, p. 940). The point is, when a narrative becomes this publicly accepted, it is difficult to sort out truth and reality without going back to original sources of information and evidence.

What May or May Not Be Empirical Literature: Literature Reviews

Investigators typically engage in a review of existing literature as they develop their own research studies. The review informs them about where knowledge gaps exist, methods previously employed by other scholars, limitations of prior work, and previous scholars’ recommendations for directing future research. These reviews may appear as a published article, without new study data being reported (see Fields, Anderson, & Dabelko-Schoeny, 2014 for example). Or, the literature review may appear in the introduction to their own empirical study report. These literature reviews are not considered to be empirical evidence sources themselves, although they may be based on empirical evidence sources. One reason is that the authors of a literature review may or may not have engaged in a systematic search process, identifying a full, rich, multi-sided pool of evidence reports.

There is, however, a type of review that applies systematic methods and is, therefore, considered to be more strongly rooted in evidence: the systematic review .

Systematic review of literature. A systematic reviewis a type of literature report where established methods have been systematically applied, objectively, in locating and synthesizing a body of literature. The systematic review report is characterized by a great deal of transparency about the methods used and the decisions made in the review process, and are replicable. Thus, it meets the criteria for empirical literature: systematic observation and methodology, objectivity, and transparency/reproducibility. We will work a great deal more with systematic reviews in the second course, SWK 3402, since they are important tools for understanding interventions. They are somewhat less common, but not unheard of, in helping us understand diverse populations, social work problems, and social phenomena.

Locating Empirical Evidence

Social workers have available a wide array of tools and resources for locating empirical evidence in the literature. These can be organized into four general categories.

Journal Articles. A number of professional journals publish articles where investigators report on the results of their empirical studies. However, it is important to know how to distinguish between empirical and non-empirical manuscripts in these journals. A key indicator, though not the only one, involves a peer review process . Many professional journals require that manuscripts undergo a process of peer review before they are accepted for publication. This means that the authors’ work is shared with scholars who provide feedback to the journal editor as to the quality of the submitted manuscript. The editor then makes a decision based on the reviewers’ feedback:

• Accept as is
• Accept with minor revisions
• Request that a revision be resubmitted (no assurance of acceptance)

When a “revise and resubmit” decision is made, the piece will go back through the review process to determine if it is now acceptable for publication and that all of the reviewers’ concerns have been adequately addressed. Editors may also reject a manuscript because it is a poor fit for the journal, based on its mission and audience, rather than sending it for review consideration.

Indicators of journal relevance. Various journals are not equally relevant to every type of question being asked of the literature. Journals may overlap to a great extent in terms of the topics they might cover; in other words, a topic might appear in multiple different journals, depending on how the topic was being addressed. For example, articles that might help answer a question about the relationship between community poverty and violence exposure might appear in several different journals, some with a focus on poverty, others with a focus on violence, and still others on community development or public health. Journal titles are sometimes a good starting point but may not give a broad enough picture of what they cover in their contents.

In focusing a literature search, it also helps to review a journal’s mission and target audience. For example, at least four different journals focus specifically on poverty:

• Journal of Children & Poverty
• Journal of Poverty
• Journal of Poverty and Social Justice
• Poverty & Public Policy

Let’s look at an example using the Journal of Poverty and Social Justice . Information about this journal is located on the journal’s webpage: http://policy.bristoluniversitypress.co.uk/journals/journal-of-poverty-and-social-justice . In the section headed “About the Journal” you can see that it is an internationally focused research journal, and that it addresses social justice issues in addition to poverty alone. The research articles are peer-reviewed (there appear to be non-empirical discussions published, as well). These descriptions about a journal are almost always available, sometimes listed as “scope” or “mission.” These descriptions also indicate the sponsorship of the journal—sponsorship may be institutional (a particular university or agency, such as Smith College Studies in Social Work ), a professional organization, such as the Council on Social Work Education (CSWE) or the National Association of Social Work (NASW), or a publishing company (e.g., Taylor & Frances, Wiley, or Sage).

Indicators of journal caliber.  Despite engaging in a peer review process, not all journals are equally rigorous. Some journals have very high rejection rates, meaning that many submitted manuscripts are rejected; others have fairly high acceptance rates, meaning that relatively few manuscripts are rejected. This is not necessarily the best indicator of quality, however, since newer journals may not be sufficiently familiar to authors with high quality manuscripts and some journals are very specific in terms of what they publish. Another index that is sometimes used is the journal’s impact factor . Impact factor is a quantitative number indicative of how often articles published in the journal are cited in the reference list of other journal articles—the statistic is calculated as the number of times on average each article published in a particular year were cited divided by the number of articles published (the number that could be cited). For example, the impact factor for the Journal of Poverty and Social Justice in our list above was 0.70 in 2017, and for the Journal of Poverty was 0.30. These are relatively low figures compared to a journal like the New England Journal of Medicine with an impact factor of 59.56! This means that articles published in that journal were, on average, cited more than 59 times in the next year or two.

Impact factors are not necessarily the best indicator of caliber, however, since many strong journals are geared toward practitioners rather than scholars, so they are less likely to be cited by other scholars but may have a large impact on a large readership. This may be the case for a journal like the one titled Social Work, the official journal of the National Association of Social Workers. It is distributed free to all members: over 120,000 practitioners, educators, and students of social work world-wide. The journal has a recent impact factor of.790. The journals with social work relevant content have impact factors in the range of 1.0 to 3.0 according to Scimago Journal & Country Rank (SJR), particularly when they are interdisciplinary journals (for example, Child Development , Journal of Marriage and Family , Child Abuse and Neglect , Child Maltreatmen t, Social Service Review , and British Journal of Social Work ). Once upon a time, a reader could locate different indexes comparing the “quality” of social work-related journals. However, the concept of “quality” is difficult to systematically define. These indexes have mostly been replaced by impact ratings, which are not necessarily the best, most robust indicators on which to rely in assessing journal quality. For example, new journals addressing cutting edge topics have not been around long enough to have been evaluated using this particular tool, and it takes a few years for articles to begin to be cited in other, later publications.

Beware of pseudo-, illegitimate, misleading, deceptive, and suspicious journals . Another side effect of living in the Age of Information is that almost anyone can circulate almost anything and call it whatever they wish. This goes for “journal” publications, as well. With the advent of open-access publishing in recent years (electronic resources available without subscription), we have seen an explosion of what are called predatory or junk journals . These are publications calling themselves journals, often with titles very similar to legitimate publications and often with fake editorial boards. These “publications” lack the integrity of legitimate journals. This caution is reminiscent of the discussions earlier in the course about pseudoscience and “snake oil” sales. The predatory nature of many apparent information dissemination outlets has to do with how scientists and scholars may be fooled into submitting their work, often paying to have their work peer-reviewed and published. There exists a “thriving black-market economy of publishing scams,” and at least two “journal blacklists” exist to help identify and avoid these scam journals (Anderson, 2017).

This issue is important to information consumers, because it creates a challenge in terms of identifying legitimate sources and publications. The challenge is particularly important to address when information from on-line, open-access journals is being considered. Open-access is not necessarily a poor choice—legitimate scientists may pay sizeable fees to legitimate publishers to make their work freely available and accessible as open-access resources. On-line access is also not necessarily a poor choice—legitimate publishers often make articles available on-line to provide timely access to the content, especially when publishing the article in hard copy will be delayed by months or even a year or more. On the other hand, stating that a journal engages in a peer-review process is no guarantee of quality—this claim may or may not be truthful. Pseudo- and junk journals may engage in some quality control practices, but may lack attention to important quality control processes, such as managing conflict of interest, reviewing content for objectivity or quality of the research conducted, or otherwise failing to adhere to industry standards (Laine & Winker, 2017).

One resource designed to assist with the process of deciphering legitimacy is the Directory of Open Access Journals (DOAJ). The DOAJ is not a comprehensive listing of all possible legitimate open-access journals, and does not guarantee quality, but it does help identify legitimate sources of information that are openly accessible and meet basic legitimacy criteria. It also is about open-access journals, not the many journals published in hard copy.

An additional caution: Search for article corrections. Despite all of the careful manuscript review and editing, sometimes an error appears in a published article. Most journals have a practice of publishing corrections in future issues. When you locate an article, it is helpful to also search for updates. Here is an example where data presented in an article’s original tables were erroneous, and a correction appeared in a later issue.

• Marchant, A., Hawton, K., Stewart A., Montgomery, P., Singaravelu, V., Lloyd, K., Purdy, N., Daine, K., & John, A. (2017). A systematic review of the relationship between internet use, self-harm and suicidal behaviour in young people: The good, the bad and the unknown. PLoS One, 12(8): e0181722. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5558917/
• Marchant, A., Hawton, K., Stewart A., Montgomery, P., Singaravelu, V., Lloyd, K., Purdy, N., Daine, K., & John, A. (2018).Correction—A systematic review of the relationship between internet use, self-harm and suicidal behaviour in young people: The good, the bad and the unknown. PLoS One, 13(3): e0193937.  http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0193937

Search Tools. In this age of information, it is all too easy to find items—the problem lies in sifting, sorting, and managing the vast numbers of items that can be found. For example, a simple Google® search for the topic “community poverty and violence” resulted in about 15,600,000 results! As a means of simplifying the process of searching for journal articles on a specific topic, a variety of helpful tools have emerged. One type of search tool has previously applied a filtering process for you: abstracting and indexing databases . These resources provide the user with the results of a search to which records have already passed through one or more filters. For example, PsycINFO is managed by the American Psychological Association and is devoted to peer-reviewed literature in behavioral science. It contains almost 4.5 million records and is growing every month. However, it may not be available to users who are not affiliated with a university library. Conducting a basic search for our topic of “community poverty and violence” in PsychINFO returned 1,119 articles. Still a large number, but far more manageable. Additional filters can be applied, such as limiting the range in publication dates, selecting only peer reviewed items, limiting the language of the published piece (English only, for example), and specified types of documents (either chapters, dissertations, or journal articles only, for example). Adding the filters for English, peer-reviewed journal articles published between 2010 and 2017 resulted in 346 documents being identified.

Just as was the case with journals, not all abstracting and indexing databases are equivalent. There may be overlap between them, but none is guaranteed to identify all relevant pieces of literature. Here are some examples to consider, depending on the nature of the questions asked of the literature:

• Academic Search Complete—multidisciplinary index of 9,300 peer-reviewed journals
• AgeLine—multidisciplinary index of aging-related content for over 600 journals
• Campbell Collaboration—systematic reviews in education, crime and justice, social welfare, international development
• Google Scholar—broad search tool for scholarly literature across many disciplines
• MEDLINE/ PubMed—National Library of medicine, access to over 15 million citations
• Oxford Bibliographies—annotated bibliographies, each is discipline specific (e.g., psychology, childhood studies, criminology, social work, sociology)
• PsycINFO/PsycLIT—international literature on material relevant to psychology and related disciplines
• SocINDEX—publications in sociology
• Social Sciences Abstracts—multiple disciplines
• Social Work Abstracts—many areas of social work are covered
• Web of Science—a “meta” search tool that searches other search tools, multiple disciplines

Placing our search for information about “community violence and poverty” into the Social Work Abstracts tool with no additional filters resulted in a manageable 54-item list. Finally, abstracting and indexing databases are another way to determine journal legitimacy: if a journal is indexed in a one of these systems, it is likely a legitimate journal. However, the converse is not necessarily true: if a journal is not indexed does not mean it is an illegitimate or pseudo-journal.

Government Sources. A great deal of information is gathered, analyzed, and disseminated by various governmental branches at the international, national, state, regional, county, and city level. Searching websites that end in.gov is one way to identify this type of information, often presented in articles, news briefs, and statistical reports. These government sources gather information in two ways: they fund external investigations through grants and contracts and they conduct research internally, through their own investigators. Here are some examples to consider, depending on the nature of the topic for which information is sought:

• Agency for Healthcare Research and Quality (AHRQ) at https://www.ahrq.gov/
• Bureau of Justice Statistics (BJS) at https://www.bjs.gov/
• Census Bureau at https://www.census.gov
• Morbidity and Mortality Weekly Report of the CDC (MMWR-CDC) at https://www.cdc.gov/mmwr/index.html
• Child Welfare Information Gateway at https://www.childwelfare.gov
• Children’s Bureau/Administration for Children & Families at https://www.acf.hhs.gov
• Forum on Child and Family Statistics at https://www.childstats.gov
• National Institutes of Health (NIH) at https://www.nih.gov , including (not limited to):
• National Institute on Aging (NIA at https://www.nia.nih.gov
• National Institute on Alcohol Abuse and Alcoholism (NIAAA) at https://www.niaaa.nih.gov
• National Institute of Child Health and Human Development (NICHD) at https://www.nichd.nih.gov
• National Institute on Drug Abuse (NIDA) at https://www.nida.nih.gov
• National Institute of Environmental Health Sciences at https://www.niehs.nih.gov
• National Institute of Mental Health (NIMH) at https://www.nimh.nih.gov
• National Institute on Minority Health and Health Disparities at https://www.nimhd.nih.gov
• National Institute of Justice (NIJ) at https://www.nij.gov
• Substance Abuse and Mental Health Services Administration (SAMHSA) at https://www.samhsa.gov/
• United States Agency for International Development at https://usaid.gov

Each state and many counties or cities have similar data sources and analysis reports available, such as Ohio Department of Health at https://www.odh.ohio.gov/healthstats/dataandstats.aspx and Franklin County at https://statisticalatlas.com/county/Ohio/Franklin-County/Overview . Data are available from international/global resources (e.g., United Nations and World Health Organization), as well.

Other Sources. The Health and Medicine Division (HMD) of the National Academies—previously the Institute of Medicine (IOM)—is a nonprofit institution that aims to provide government and private sector policy and other decision makers with objective analysis and advice for making informed health decisions. For example, in 2018 they produced reports on topics in substance use and mental health concerning the intersection of opioid use disorder and infectious disease,  the legal implications of emerging neurotechnologies, and a global agenda concerning the identification and prevention of violence (see http://www.nationalacademies.org/hmd/Global/Topics/Substance-Abuse-Mental-Health.aspx ). The exciting aspect of this resource is that it addresses many topics that are current concerns because they are hoping to help inform emerging policy. The caution to consider with this resource is the evidence is often still emerging, as well.

Numerous “think tank” organizations exist, each with a specific mission. For example, the Rand Corporation is a nonprofit organization offering research and analysis to address global issues since 1948. The institution’s mission is to help improve policy and decision making “to help individuals, families, and communities throughout the world be safer and more secure, healthier and more prosperous,” addressing issues of energy, education, health care, justice, the environment, international affairs, and national security (https://www.rand.org/about/history.html). And, for example, the Robert Woods Johnson Foundation is a philanthropic organization supporting research and research dissemination concerning health issues facing the United States. The foundation works to build a culture of health across systems of care (not only medical care) and communities (https://www.rwjf.org).

While many of these have a great deal of helpful evidence to share, they also may have a strong political bias. Objectivity is often lacking in what information these organizations provide: they provide evidence to support certain points of view. That is their purpose—to provide ideas on specific problems, many of which have a political component. Think tanks “are constantly researching solutions to a variety of the world’s problems, and arguing, advocating, and lobbying for policy changes at local, state, and federal levels” (quoted from https://thebestschools.org/features/most-influential-think-tanks/ ). Helpful information about what this one source identified as the 50 most influential U.S. think tanks includes identifying each think tank’s political orientation. For example, The Heritage Foundation is identified as conservative, whereas Human Rights Watch is identified as liberal.

While not the same as think tanks, many mission-driven organizations also sponsor or report on research, as well. For example, the National Association for Children of Alcoholics (NACOA) in the United States is a registered nonprofit organization. Its mission, along with other partnering organizations, private-sector groups, and federal agencies, is to promote policy and program development in research, prevention and treatment to provide information to, for, and about children of alcoholics (of all ages). Based on this mission, the organization supports knowledge development and information gathering on the topic and disseminates information that serves the needs of this population. While this is a worthwhile mission, there is no guarantee that the information meets the criteria for evidence with which we have been working. Evidence reported by think tank and mission-driven sources must be utilized with a great deal of caution and critical analysis!

In many instances an empirical report has not appeared in the published literature, but in the form of a technical or final report to the agency or program providing the funding for the research that was conducted. One such example is presented by a team of investigators funded by the National Institute of Justice to evaluate a program for training professionals to collect strong forensic evidence in instances of sexual assault (Patterson, Resko, Pierce-Weeks, & Campbell, 2014): https://www.ncjrs.gov/pdffiles1/nij/grants/247081.pdf . Investigators may serve in the capacity of consultant to agencies, programs, or institutions, and provide empirical evidence to inform activities and planning. One such example is presented by Maguire-Jack (2014) as a report to a state’s child maltreatment prevention board: https://preventionboard.wi.gov/Documents/InvestmentInPreventionPrograming_Final.pdf .

When Direct Answers to Questions Cannot Be Found. Sometimes social workers are interested in finding answers to complex questions or questions related to an emerging, not-yet-understood topic. This does not mean giving up on empirical literature. Instead, it requires a bit of creativity in approaching the literature. A Venn diagram might help explain this process. Consider a scenario where a social worker wishes to locate literature to answer a question concerning issues of intersectionality. Intersectionality is a social justice term applied to situations where multiple categorizations or classifications come together to create overlapping, interconnected, or multiplied disadvantage. For example, women with a substance use disorder and who have been incarcerated face a triple threat in terms of successful treatment for a substance use disorder: intersectionality exists between being a woman, having a substance use disorder, and having been in jail or prison. After searching the literature, little or no empirical evidence might have been located on this specific triple-threat topic. Instead, the social worker will need to seek literature on each of the threats individually, and possibly will find literature on pairs of topics (see Figure 3-1). There exists some literature about women’s outcomes for treatment of a substance use disorder (a), some literature about women during and following incarceration (b), and some literature about substance use disorders and incarceration (c). Despite not having a direct line on the center of the intersecting spheres of literature (d), the social worker can develop at least a partial picture based on the overlapping literatures.

Figure 3-1. Venn diagram of intersecting literature sets.

Take a moment to complete the following activity. For each statement about empirical literature, decide if it is true or false.

Social Work 3401 Coursebook Copyright © by Dr. Audrey Begun is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License , except where otherwise noted.

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Writing a Literature Review

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A literature review is a document or section of a document that collects key sources on a topic and discusses those sources in conversation with each other (also called synthesis ). The lit review is an important genre in many disciplines, not just literature (i.e., the study of works of literature such as novels and plays). When we say “literature review” or refer to “the literature,” we are talking about the research ( scholarship ) in a given field. You will often see the terms “the research,” “the scholarship,” and “the literature” used mostly interchangeably.

Where, when, and why would I write a lit review?

There are a number of different situations where you might write a literature review, each with slightly different expectations; different disciplines, too, have field-specific expectations for what a literature review is and does. For instance, in the humanities, authors might include more overt argumentation and interpretation of source material in their literature reviews, whereas in the sciences, authors are more likely to report study designs and results in their literature reviews; these differences reflect these disciplines’ purposes and conventions in scholarship. You should always look at examples from your own discipline and talk to professors or mentors in your field to be sure you understand your discipline’s conventions, for literature reviews as well as for any other genre.

A literature review can be a part of a research paper or scholarly article, usually falling after the introduction and before the research methods sections. In these cases, the lit review just needs to cover scholarship that is important to the issue you are writing about; sometimes it will also cover key sources that informed your research methodology.

Lit reviews can also be standalone pieces, either as assignments in a class or as publications. In a class, a lit review may be assigned to help students familiarize themselves with a topic and with scholarship in their field, get an idea of the other researchers working on the topic they’re interested in, find gaps in existing research in order to propose new projects, and/or develop a theoretical framework and methodology for later research. As a publication, a lit review usually is meant to help make other scholars’ lives easier by collecting and summarizing, synthesizing, and analyzing existing research on a topic. This can be especially helpful for students or scholars getting into a new research area, or for directing an entire community of scholars toward questions that have not yet been answered.

What are the parts of a lit review?

Most lit reviews use a basic introduction-body-conclusion structure; if your lit review is part of a larger paper, the introduction and conclusion pieces may be just a few sentences while you focus most of your attention on the body. If your lit review is a standalone piece, the introduction and conclusion take up more space and give you a place to discuss your goals, research methods, and conclusions separately from where you discuss the literature itself.

Introduction:

• An introductory paragraph that explains what your working topic and thesis is
• A forecast of key topics or texts that will appear in the review
• Potentially, a description of how you found sources and how you analyzed them for inclusion and discussion in the review (more often found in published, standalone literature reviews than in lit review sections in an article or research paper)
• Summarize and synthesize: Give an overview of the main points of each source and combine them into a coherent whole
• Analyze and interpret: Don’t just paraphrase other researchers – add your own interpretations where possible, discussing the significance of findings in relation to the literature as a whole
• Critically Evaluate: Mention the strengths and weaknesses of your sources
• Write in well-structured paragraphs: Use transition words and topic sentence to draw connections, comparisons, and contrasts.

Conclusion:

• Summarize the key findings you have taken from the literature and emphasize their significance
• Connect it back to your primary research question

How should I organize my lit review?

Lit reviews can take many different organizational patterns depending on what you are trying to accomplish with the review. Here are some examples:

• Chronological : The simplest approach is to trace the development of the topic over time, which helps familiarize the audience with the topic (for instance if you are introducing something that is not commonly known in your field). If you choose this strategy, be careful to avoid simply listing and summarizing sources in order. Try to analyze the patterns, turning points, and key debates that have shaped the direction of the field. Give your interpretation of how and why certain developments occurred (as mentioned previously, this may not be appropriate in your discipline — check with a teacher or mentor if you’re unsure).
• Thematic : If you have found some recurring central themes that you will continue working with throughout your piece, you can organize your literature review into subsections that address different aspects of the topic. For example, if you are reviewing literature about women and religion, key themes can include the role of women in churches and the religious attitude towards women.
• Qualitative versus quantitative research
• Empirical versus theoretical scholarship
• Divide the research by sociological, historical, or cultural sources
• Theoretical : In many humanities articles, the literature review is the foundation for the theoretical framework. You can use it to discuss various theories, models, and definitions of key concepts. You can argue for the relevance of a specific theoretical approach or combine various theorical concepts to create a framework for your research.

What are some strategies or tips I can use while writing my lit review?

Any lit review is only as good as the research it discusses; make sure your sources are well-chosen and your research is thorough. Don’t be afraid to do more research if you discover a new thread as you’re writing. More info on the research process is available in our "Conducting Research" resources .

As you’re doing your research, create an annotated bibliography ( see our page on the this type of document ). Much of the information used in an annotated bibliography can be used also in a literature review, so you’ll be not only partially drafting your lit review as you research, but also developing your sense of the larger conversation going on among scholars, professionals, and any other stakeholders in your topic.

Usually you will need to synthesize research rather than just summarizing it. This means drawing connections between sources to create a picture of the scholarly conversation on a topic over time. Many student writers struggle to synthesize because they feel they don’t have anything to add to the scholars they are citing; here are some strategies to help you:

• It often helps to remember that the point of these kinds of syntheses is to show your readers how you understand your research, to help them read the rest of your paper.
• Writing teachers often say synthesis is like hosting a dinner party: imagine all your sources are together in a room, discussing your topic. What are they saying to each other?
• Look at the in-text citations in each paragraph. Are you citing just one source for each paragraph? This usually indicates summary only. When you have multiple sources cited in a paragraph, you are more likely to be synthesizing them (not always, but often
• Read more about synthesis here.

The most interesting literature reviews are often written as arguments (again, as mentioned at the beginning of the page, this is discipline-specific and doesn’t work for all situations). Often, the literature review is where you can establish your research as filling a particular gap or as relevant in a particular way. You have some chance to do this in your introduction in an article, but the literature review section gives a more extended opportunity to establish the conversation in the way you would like your readers to see it. You can choose the intellectual lineage you would like to be part of and whose definitions matter most to your thinking (mostly humanities-specific, but this goes for sciences as well). In addressing these points, you argue for your place in the conversation, which tends to make the lit review more compelling than a simple reporting of other sources.

• Meriam Library

SWRK 330 - Social Work Research Methods

• Literature Reviews and Empirical Research
• Databases and Search Tips
• Article Citations
• Scholarly Journal Evaulation
• Statistical Sources
• Books and eBooks

What is a Literature Review?

Empirical research.

• Annotated Bibliographies

A literature review  summarizes and discusses previous publications  on a topic.

It should also:

explore past research and its strengths and weaknesses.

be used to validate the target and methods you have chosen for your proposed research.

consist of books and scholarly journals that provide research examples of populations or settings similar to your own, as well as community resources to document the need for your proposed research.

The literature review does not present new  primary  scholarship. 

be completed in the correct citation format requested by your professor  (see the  C itations Tab)

Access Purdue  OWL's Social Work Literature Review Guidelines here .  

Empirical Research  is  research  that is based on experimentation or observation, i.e. Evidence. Such  research  is often conducted to answer a specific question or to test a hypothesis (educated guess).

How do you know if a study is empirical? Read the subheadings within the article, book, or report and look for a description of the research "methodology."  Ask yourself: Could I recreate this study and test these results?

These are some key features to look for when identifying empirical research.

NOTE:  Not all of these features will be in every empirical research article, some may be excluded, use this only as a guide.

• Statement of methodology
• Research questions are clear and measurable
• Individuals, group, subjects which are being studied are identified/defined
• Data is presented regarding the findings
• Controls or instruments such as surveys or tests were conducted
• There is a literature review
• There is discussion of the results included
• Citations/references are included

See also Empirical Research Guide

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• Conceptual review
• Theoretical review,
• Empirical review or review of empirical works of literature/studies, and lastly
• Conclusion or Summary of the literature reviewed.
• Decide on a topic
• Highlight the studies/literature that you will review in the empirical review
• Analyze the works of literature separately.
• Summarize the literature in table or concept map format.
• Synthesize the literature and then proceed to write your empirical review.

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Ten Simple Rules for Writing a Literature Review

Marco pautasso.

1 Centre for Functional and Evolutionary Ecology (CEFE), CNRS, Montpellier, France

2 Centre for Biodiversity Synthesis and Analysis (CESAB), FRB, Aix-en-Provence, France

Literature reviews are in great demand in most scientific fields. Their need stems from the ever-increasing output of scientific publications [1] . For example, compared to 1991, in 2008 three, eight, and forty times more papers were indexed in Web of Science on malaria, obesity, and biodiversity, respectively [2] . Given such mountains of papers, scientists cannot be expected to examine in detail every single new paper relevant to their interests [3] . Thus, it is both advantageous and necessary to rely on regular summaries of the recent literature. Although recognition for scientists mainly comes from primary research, timely literature reviews can lead to new synthetic insights and are often widely read [4] . For such summaries to be useful, however, they need to be compiled in a professional way [5] .

When starting from scratch, reviewing the literature can require a titanic amount of work. That is why researchers who have spent their career working on a certain research issue are in a perfect position to review that literature. Some graduate schools are now offering courses in reviewing the literature, given that most research students start their project by producing an overview of what has already been done on their research issue [6] . However, it is likely that most scientists have not thought in detail about how to approach and carry out a literature review.

Reviewing the literature requires the ability to juggle multiple tasks, from finding and evaluating relevant material to synthesising information from various sources, from critical thinking to paraphrasing, evaluating, and citation skills [7] . In this contribution, I share ten simple rules I learned working on about 25 literature reviews as a PhD and postdoctoral student. Ideas and insights also come from discussions with coauthors and colleagues, as well as feedback from reviewers and editors.

Rule 1: Define a Topic and Audience

How to choose which topic to review? There are so many issues in contemporary science that you could spend a lifetime of attending conferences and reading the literature just pondering what to review. On the one hand, if you take several years to choose, several other people may have had the same idea in the meantime. On the other hand, only a well-considered topic is likely to lead to a brilliant literature review [8] . The topic must at least be:

• interesting to you (ideally, you should have come across a series of recent papers related to your line of work that call for a critical summary),
• an important aspect of the field (so that many readers will be interested in the review and there will be enough material to write it), and
• a well-defined issue (otherwise you could potentially include thousands of publications, which would make the review unhelpful).

Ideas for potential reviews may come from papers providing lists of key research questions to be answered [9] , but also from serendipitous moments during desultory reading and discussions. In addition to choosing your topic, you should also select a target audience. In many cases, the topic (e.g., web services in computational biology) will automatically define an audience (e.g., computational biologists), but that same topic may also be of interest to neighbouring fields (e.g., computer science, biology, etc.).

Rule 2: Search and Re-search the Literature

After having chosen your topic and audience, start by checking the literature and downloading relevant papers. Five pieces of advice here:

• keep track of the search items you use (so that your search can be replicated [10] ),
• keep a list of papers whose pdfs you cannot access immediately (so as to retrieve them later with alternative strategies),
• use a paper management system (e.g., Mendeley, Papers, Qiqqa, Sente),
• define early in the process some criteria for exclusion of irrelevant papers (these criteria can then be described in the review to help define its scope), and
• do not just look for research papers in the area you wish to review, but also seek previous reviews.

The chances are high that someone will already have published a literature review ( Figure 1 ), if not exactly on the issue you are planning to tackle, at least on a related topic. If there are already a few or several reviews of the literature on your issue, my advice is not to give up, but to carry on with your own literature review,

The bottom-right situation (many literature reviews but few research papers) is not just a theoretical situation; it applies, for example, to the study of the impacts of climate change on plant diseases, where there appear to be more literature reviews than research studies [33] .

• discussing in your review the approaches, limitations, and conclusions of past reviews,
• trying to find a new angle that has not been covered adequately in the previous reviews, and
• incorporating new material that has inevitably accumulated since their appearance.

When searching the literature for pertinent papers and reviews, the usual rules apply:

• be thorough,
• use different keywords and database sources (e.g., DBLP, Google Scholar, ISI Proceedings, JSTOR Search, Medline, Scopus, Web of Science), and
• look at who has cited past relevant papers and book chapters.

Rule 3: Take Notes While Reading

If you read the papers first, and only afterwards start writing the review, you will need a very good memory to remember who wrote what, and what your impressions and associations were while reading each single paper. My advice is, while reading, to start writing down interesting pieces of information, insights about how to organize the review, and thoughts on what to write. This way, by the time you have read the literature you selected, you will already have a rough draft of the review.

Of course, this draft will still need much rewriting, restructuring, and rethinking to obtain a text with a coherent argument [11] , but you will have avoided the danger posed by staring at a blank document. Be careful when taking notes to use quotation marks if you are provisionally copying verbatim from the literature. It is advisable then to reformulate such quotes with your own words in the final draft. It is important to be careful in noting the references already at this stage, so as to avoid misattributions. Using referencing software from the very beginning of your endeavour will save you time.

Rule 4: Choose the Type of Review You Wish to Write

After having taken notes while reading the literature, you will have a rough idea of the amount of material available for the review. This is probably a good time to decide whether to go for a mini- or a full review. Some journals are now favouring the publication of rather short reviews focusing on the last few years, with a limit on the number of words and citations. A mini-review is not necessarily a minor review: it may well attract more attention from busy readers, although it will inevitably simplify some issues and leave out some relevant material due to space limitations. A full review will have the advantage of more freedom to cover in detail the complexities of a particular scientific development, but may then be left in the pile of the very important papers “to be read” by readers with little time to spare for major monographs.

There is probably a continuum between mini- and full reviews. The same point applies to the dichotomy of descriptive vs. integrative reviews. While descriptive reviews focus on the methodology, findings, and interpretation of each reviewed study, integrative reviews attempt to find common ideas and concepts from the reviewed material [12] . A similar distinction exists between narrative and systematic reviews: while narrative reviews are qualitative, systematic reviews attempt to test a hypothesis based on the published evidence, which is gathered using a predefined protocol to reduce bias [13] , [14] . When systematic reviews analyse quantitative results in a quantitative way, they become meta-analyses. The choice between different review types will have to be made on a case-by-case basis, depending not just on the nature of the material found and the preferences of the target journal(s), but also on the time available to write the review and the number of coauthors [15] .

Rule 5: Keep the Review Focused, but Make It of Broad Interest

Whether your plan is to write a mini- or a full review, it is good advice to keep it focused 16 , 17 . Including material just for the sake of it can easily lead to reviews that are trying to do too many things at once. The need to keep a review focused can be problematic for interdisciplinary reviews, where the aim is to bridge the gap between fields [18] . If you are writing a review on, for example, how epidemiological approaches are used in modelling the spread of ideas, you may be inclined to include material from both parent fields, epidemiology and the study of cultural diffusion. This may be necessary to some extent, but in this case a focused review would only deal in detail with those studies at the interface between epidemiology and the spread of ideas.

While focus is an important feature of a successful review, this requirement has to be balanced with the need to make the review relevant to a broad audience. This square may be circled by discussing the wider implications of the reviewed topic for other disciplines.

Rule 6: Be Critical and Consistent

Reviewing the literature is not stamp collecting. A good review does not just summarize the literature, but discusses it critically, identifies methodological problems, and points out research gaps [19] . After having read a review of the literature, a reader should have a rough idea of:

• the major achievements in the reviewed field,
• the main areas of debate, and
• the outstanding research questions.

It is challenging to achieve a successful review on all these fronts. A solution can be to involve a set of complementary coauthors: some people are excellent at mapping what has been achieved, some others are very good at identifying dark clouds on the horizon, and some have instead a knack at predicting where solutions are going to come from. If your journal club has exactly this sort of team, then you should definitely write a review of the literature! In addition to critical thinking, a literature review needs consistency, for example in the choice of passive vs. active voice and present vs. past tense.

Rule 7: Find a Logical Structure

Like a well-baked cake, a good review has a number of telling features: it is worth the reader's time, timely, systematic, well written, focused, and critical. It also needs a good structure. With reviews, the usual subdivision of research papers into introduction, methods, results, and discussion does not work or is rarely used. However, a general introduction of the context and, toward the end, a recapitulation of the main points covered and take-home messages make sense also in the case of reviews. For systematic reviews, there is a trend towards including information about how the literature was searched (database, keywords, time limits) [20] .

How can you organize the flow of the main body of the review so that the reader will be drawn into and guided through it? It is generally helpful to draw a conceptual scheme of the review, e.g., with mind-mapping techniques. Such diagrams can help recognize a logical way to order and link the various sections of a review [21] . This is the case not just at the writing stage, but also for readers if the diagram is included in the review as a figure. A careful selection of diagrams and figures relevant to the reviewed topic can be very helpful to structure the text too [22] .

Rule 8: Make Use of Feedback

Reviews of the literature are normally peer-reviewed in the same way as research papers, and rightly so [23] . As a rule, incorporating feedback from reviewers greatly helps improve a review draft. Having read the review with a fresh mind, reviewers may spot inaccuracies, inconsistencies, and ambiguities that had not been noticed by the writers due to rereading the typescript too many times. It is however advisable to reread the draft one more time before submission, as a last-minute correction of typos, leaps, and muddled sentences may enable the reviewers to focus on providing advice on the content rather than the form.

Feedback is vital to writing a good review, and should be sought from a variety of colleagues, so as to obtain a diversity of views on the draft. This may lead in some cases to conflicting views on the merits of the paper, and on how to improve it, but such a situation is better than the absence of feedback. A diversity of feedback perspectives on a literature review can help identify where the consensus view stands in the landscape of the current scientific understanding of an issue [24] .

Rule 9: Include Your Own Relevant Research, but Be Objective

In many cases, reviewers of the literature will have published studies relevant to the review they are writing. This could create a conflict of interest: how can reviewers report objectively on their own work [25] ? Some scientists may be overly enthusiastic about what they have published, and thus risk giving too much importance to their own findings in the review. However, bias could also occur in the other direction: some scientists may be unduly dismissive of their own achievements, so that they will tend to downplay their contribution (if any) to a field when reviewing it.

In general, a review of the literature should neither be a public relations brochure nor an exercise in competitive self-denial. If a reviewer is up to the job of producing a well-organized and methodical review, which flows well and provides a service to the readership, then it should be possible to be objective in reviewing one's own relevant findings. In reviews written by multiple authors, this may be achieved by assigning the review of the results of a coauthor to different coauthors.

Rule 10: Be Up-to-Date, but Do Not Forget Older Studies

Given the progressive acceleration in the publication of scientific papers, today's reviews of the literature need awareness not just of the overall direction and achievements of a field of inquiry, but also of the latest studies, so as not to become out-of-date before they have been published. Ideally, a literature review should not identify as a major research gap an issue that has just been addressed in a series of papers in press (the same applies, of course, to older, overlooked studies (“sleeping beauties” [26] )). This implies that literature reviewers would do well to keep an eye on electronic lists of papers in press, given that it can take months before these appear in scientific databases. Some reviews declare that they have scanned the literature up to a certain point in time, but given that peer review can be a rather lengthy process, a full search for newly appeared literature at the revision stage may be worthwhile. Assessing the contribution of papers that have just appeared is particularly challenging, because there is little perspective with which to gauge their significance and impact on further research and society.

Inevitably, new papers on the reviewed topic (including independently written literature reviews) will appear from all quarters after the review has been published, so that there may soon be the need for an updated review. But this is the nature of science [27] – [32] . I wish everybody good luck with writing a review of the literature.

Acknowledgments

Many thanks to M. Barbosa, K. Dehnen-Schmutz, T. Döring, D. Fontaneto, M. Garbelotto, O. Holdenrieder, M. Jeger, D. Lonsdale, A. MacLeod, P. Mills, M. Moslonka-Lefebvre, G. Stancanelli, P. Weisberg, and X. Xu for insights and discussions, and to P. Bourne, T. Matoni, and D. Smith for helpful comments on a previous draft.

Funding Statement

This work was funded by the French Foundation for Research on Biodiversity (FRB) through its Centre for Synthesis and Analysis of Biodiversity data (CESAB), as part of the NETSEED research project. The funders had no role in the preparation of the manuscript.

Ethics in AI through the practitioner’s view: a grounded theory literature review

• Open access
• Published: 06 May 2024
• Volume 29 , article number  67 , ( 2024 )

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• Aastha Pant   ORCID: orcid.org/0000-0002-6183-0492 1 ,
• Rashina Hoda 1 ,
• Chakkrit Tantithamthavorn 1 &
• Burak Turhan 2  

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The term ethics is widely used, explored, and debated in the context of developing Artificial Intelligence (AI) based software systems. In recent years, numerous incidents have raised the profile of ethical issues in AI development and led to public concerns about the proliferation of AI technology in our everyday lives. But what do we know about the views and experiences of those who develop these systems – the AI practitioners? We conducted a grounded theory literature review (GTLR) of 38 primary empirical studies that included AI practitioners’ views on ethics in AI and analysed them to derive five categories: practitioner awareness , perception , need , challenge , and approach . These are underpinned by multiple codes and concepts that we explain with evidence from the included studies. We present a taxonomy of ethics in AI from practitioners’ viewpoints to assist AI practitioners in identifying and understanding the different aspects of AI ethics. The taxonomy provides a landscape view of the key aspects that concern AI practitioners when it comes to ethics in AI. We also share an agenda for future research studies and recommendations for practitioners, managers, and organisations to help in their efforts to better consider and implement ethics in AI.

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1 Introduction

Over the last few years, there has been a swift rise in the adoption of AI technology across diverse sectors such as health, transportation, education, IT, banking, and more. The widespread use of AI has underscored the significance of ethical considerations within the realm of AI (Hagendorff 2020 ). Ethics refers to “ the moral principles that govern the behaviors or activities of a person or a group of people ” (Nalini 2020 ). The process of attributing moral values and ethical principles to machines to resolve ethical issues they encounter, and enabling them to operate ethically is a form of applied ethics (Anderson and Anderson 2011 ). There is a lack of a universal definition of AI ethics and ethical principles (Kazim and Koshiyama 2021 ). In our study, we adopted the definition proposed by Siau and Wang ( 2020 ), stating that “AI ethics refers to the principles of developing AI to interact with other AIs and humans ethically and function ethically in society” . Likewise, we have adopted the definitions of AI ethical principles outlined in Australia’s AI Ethics Principles Footnote 1 list because there is a lack of a universal set of AI ethics principles that the whole world follows. Different countries and organisations have their own distinct AI ethical principles. For example, the European Commission has defined its own guidelines for trustworthy AI (Commission 2019 ), the United States Department of Defense has adopted 5 principles of AI Ethics (Defense 2020 ), and the Organisation for Economic Cooperation and Development (OECD) has defined its AI principles to promote the use of ethical AI (OECD 2019 ). Australia’s AI Ethics Principles address a broad spectrum of ethical concerns, spanning from human to environmental well-being. They encompass widely recognised ethical principles like fairness, privacy, and transparency, along with less common but crucial concepts such as contestability and accountability. The definitions of the terminologies used in this study have been provided in Appendix C .

The consideration of ethics in AI includes the process of development as well as the resulting product. Footnote 2 It is very important to incorporate ethical considerations in the development of AI products to ensure that the end product is ethically, socially, and legally responsible (Obermeyer and Emanuel 2016 ). The importance of ethical consideration in AI is highlighted by recent incidents that demonstrate its impact (Bostrom and Yudkowsky 2018 ). For example, GitHub was criticised for using unlicensed source code as training data for their AI product, which resulted in disappointment among software developers (Al-Kaswan and Izadi 2023 ). There were also cases of racial and gender bias in AI systems, such as facial recognition algorithms that performed better on white men and worse on black women, highlighting issues of accountability and bias (Buolamwini and Gebru 2018 ). Additionally, in 2018, Amazon had to halt the use of their AI-powered recruitment tool due to gender bias (Dastin (2018) ), and in 2020, the Dutch court halted the use of System Risk Indication (SyRI) - a secret algorithm to detect possible social welfare fraud as this algorithm lacked transparency for citizens about what it does with the personal information of the people (SyR 2020 ). In each of these examples, ethical problems might have arisen during the development process, giving rise to ethical concerns regarding the resulting product. These incidents emphasise the importance of ethical considerations in AI development.

We were motivated to study the area of ethics in AI due to various case studies and the importance of the topic. Despite the existence of ethical principles, guidelines, and company policies, the implementation of these principles is ultimately up to the AI practitioners. Thus, we became interested in conducting a review study to explore existing research on ethics in AI. Specifically, we were interested in exploring the perspectives of those closest to it – the AI practitioners, Footnote 3 as they are in a unique position to bring about changes and improvements and the need for review studies in the area of AI ethics to understand practitioners’ perspectives have also been highlighted in the literature (Khan et al. 2022 ; Leikas et al. 2019 ).

To understand practitioners’ views on AI ethics as presented in the literature, we conducted a grounded theory literature review (GTLR) following the five-step framework of define , search , select , analyse , and present proposed by Wolfswinkel et al. ( 2013 ). We first defined the overarching research question (RQ), What do we know from the literature about the AI practitioners’ views and experiences of ethics in AI? Footnote 4 Our study aimed to find empirical studies that focused on capturing the views and experiences of AI practitioners regarding AI ethics and ethical principles, and their implementation in developing AI-based systems. Then, we used the grounded theory literature review (GTLR) protocol to search and select primary research articles Footnote 5 that include practitioners’ views on AI ethics. To analyse the selected studies, we applied the procedures of socio-technical grounded theory (STGT) for data analysis (Hoda 2021 ) such as open coding , targeted coding , constant comparison , and memoing , iteratively on the 38 primary empirical studies. Wolfswinkel et al. ( 2013 ) welcome adaptations to their framework by acknowledging that “... one size does not fit all, and there should be no hesitation whatsoever to deviate from our proposed steps, as long as such variation is well motivated.” Since there was little concrete guidance available on how to perform in-depth analysis and develop theory from literature as a data source, we made some adaptations, as explained in the methodology section (Section 3 ).

Based on our analysis, we present a taxonomy of ethics in AI from practitioners’ viewpoints spanning five categories: (i) practitioner awareness, (ii) practitioner perception, (iii) practitioner need, (iv) practitioner challenge, and (v) practitioner approach , captured in Figs. 4 and 5 , and described in-depth in Sections 5 and 6.1 . The main contributions of this paper are:

A source of gathered information from literature on AI practitioners’ views and experiences of ethics in AI,

A taxonomy of ethics in AI from practitioners’ viewpoints which includes five categories such their awareness , perception , need , challenge , and approach related to ethics in AI,

An example of the application of grounded theory literature review (GTLR) in software engineering,

Guidance for practitioners who require a better understanding of the requirements and factors affecting ethics implementation in AI,

A set of recommendations for future research in the area of ethics implementation in AI from practitioners’ perspective.

The rest of the paper is structured as follows: Section 2 presents the background details in the area of ethics in Information and Communications Technology (ICT), software engineering, and AI, followed by the details of the grounded theory literature review (GTLR) methodology in Section 3 . Then, we discuss the challenges, threats, and limitations of the methodology in Section 4 , present the findings in Section 5 which is followed by the description of the taxonomy , insights, and recommendations in Section 6 . Then, we present the methodological lessons learned in Section 7 followed by a conclusion in Section 8 .

2 Background

2.1 ethics in ict and software engineering.

The topic of ‘ethics’ has been a well-researched and widely discussed topic in the field of ICT for a long time. Over recent years, various IT professional organisations worldwide, like the Association for Computing Machinery (ACM), Footnote 6 the Institute for Certification of IT Professionals (ICCP), Footnote 7 and AITP Footnote 8 have developed their own codes of ethics (Payne and Landry 2006 ). These codes of ethics in the ICT domain are created to motivate and steer the ethical behavior of all computer professionals. This includes those who are currently working in the field, those who aspire to do so, teachers, students, influencers, and anyone who makes significant use of computer technology, as defined by the Association for Computing Machinery (ACM).

In 1991, Gotterbarn ( 1991 ) expressed concern about the insufficient emphasis placed on professional ethics in guiding the daily activities of computing professionals within their respective roles. Subsequently, he actively engaged in various initiatives aimed at advocating for ethical codes and fostering a sense of professional responsibility in the field. Studies have been conducted to explore how these codes of ethics affect the decision-making of professionals in the ICT sector. Ethics within the professional sphere can significantly aid ICT professionals in their decision-making, as evidenced by research conducted by Allen et al. ( 2011 ), and these codes have been observed to influence the conduct of ICT professionals (Harrington 1996 ). In 2010, Van den Bergh and Deschoolmeester ( 2010 ) conducted a survey involving 276 ICT professionals to explore the potential value of ethical codes of conduct for the ICT industry in dealing with contentious issues. They concluded that having a policy regarding ICT ethics does indeed significantly influence how professionals assess ethical or unethical situations in some cases. Fleischmann et al. ( 2017 ) conducted a mixed-method study with ICT professionals on the role of codes of ethics and the relationship between their experiences and attitudes towards the codes of ethics.

Likewise, studies have been conducted to investigate the impact of ethics in the area of Software Engineering. Rashid et al. ( 2009 ) concluded that ethics has been a very important part of software engineering and discussed the ethical challenges of software engineers who design systems for the digital world. Aydemir and Dalpiaz ( 2018 ) introduced an analytical framework to aid stakeholders including users and developers in capturing and analysing ethical requirements to foster ethical alignment within software artifacts and the development processes. In a similar vein, according to Pierce and Henry ( 1996 ), one’s personal ethical principles, workplace ethics, and adherence to formal codes of conduct all play a significant role in influencing the ethical conduct of software professionals. Pierce and Henry ( 1996 ) also delves into the extent of influence exerted by these three factors. On a related note, Hall ( 2009 ) examines the concept of ethical conduct in the context of software engineers, emphasizing the importance of good professional ethics. Furthermore, in a study by Fraga ( 2022 ), they conducted a survey involving software engineering professionals to explore the role of ethics in their field. The findings of the study suggest that the promotion of ethical leadership among systems engineers can be achieved when they adhere to established standards, codes, and ethical principles. These studies into ethics within the realms of ICT and Software Engineering indicate that this subject has been of significant importance for a long time, and there has been a prolonged effort to improve ethical considerations in these fields.

In summary, there is a recognised need for a stronger focus on professional ethics in guiding the daily activities of computing professionals. Multiple studies consistently demonstrate the substantial influence of ethical codes on decision-making in the ICT sector and Software Engineering, shaping behavior and ethical assessments. The collective findings underscore the importance of ethical considerations in the fields of ICT and Software Engineering.

2.2 Secondary Studies on AI Ethics

A number of secondary studies have been conducted that focused on the theme of investigating the ethical principles and guidelines related to AI. For example, Khan et al. ( 2022 ) conducted a Systematic Literature Review (SLR) to investigate the agreement on the significance of AI ethical principles and identify potential challenges to their adoption. They found that the most common AI ethics principles are transparency, privacy, accountability, and fairness. However, significant challenges in incorporating ethics into AI include a lack of ethical knowledge and vague principles. Likewise, Ryan and Stahl ( 2020 ) conducted a review study to provide a comprehensive analysis of the normative consequences associated with current AI ethics guidelines, specifically targeting AI developers and organisational users. Lu et al. ( 2022 ) conducted a Systematic Literature Review (SLR) to identify the responsible AI principles discussed in the existing literature and to uncover potential solutions for responsible AI. Additionally, they outlined a research roadmap for the field of software engineering with a focus on responsible AI.

Likewise, review studies have been conducted to investigate the ethical concerns of the use of AI in different domains. Möllmann et al. ( 2021 ) conducted a Systematic Literature Review (SLR) to explore which ethical considerations of AI are being investigated in digital health and classified the relevant literature based on the five ethical principles of AI including beneficence,non-maleficence, autonomy, justice, and explicability . Likewise, Royakkers et al. ( 2018 ) conducted an SLR to explore the social and ethical issues that arise due to digitization based on six different technologies like Internet of Things, robotics, bio-metrics, persuasive technology, virtual & augmented reality, and digital platforms. The review uncovered recurring themes such as privacy, security, autonomy, justice, human dignity, control of technology, and the balance of powers.

Studies have also been conducted to explore different methods and approaches to enhance the ethical development of AI. For example, Wiese et al. ( 2023 ) conducted a Systematic Literature Review (SLR) to explore the methods to promote and engage practice on the front end of ethical and responsible AI. The study was guided by an adaption of the PRISMA framework and Hess & Fore’s 2017 methodological approach. Morley et al. ( 2020 ) conducted a review study with the aim of exploring AI ethics tools, methods, and research that are accessible to the public, for translating ethical principles into practice.

Most of the secondary studies have either focused on investigating specific AI ethical principles, the ethical consequences of AI systems, or the approaches to enhance the ethical development of AI. Conducting a review study to identify and analyse primary empirical research on AI practitioners’ perspectives regarding AI ethics is important for gaining an understanding of the ethical landscape in the field of AI. It can also inform practical interventions, contribute to policy development, and guide educational initiatives aimed at promoting responsible and ethical practices in the development and deployment of AI technologies.

2.3 Ethics in AI

There are numerous and divergent views on the topic of ethics in AI (Vakkuri et al. 2020b ; Mittelstadt 2019 ; Hagendorff 2020 ), as it has been increasingly applied in various contexts and industries (Kessing 2021 ). AI practitioners and researchers seem to have mixed perspectives about AI ethics. Some believe there is no rush to consider AI-related ethical issues as AI has a long way from being comparable to human capabilities and behaviors (Siau and Wang 2020 ), while others conclude that AI systems must be developed by considering ethics as they can have enormous societal impact (Bostrom and Yudkowsky 2018 ; Bryson and Winfield 2017 ). Although the viewpoints vary from practitioner to practitioner, most conclude that AI ethics is an emerging and widely discussed topic and a current relevant issue of the real world (Vainio-Pekka 2020 ). This indicates that while opinions on the importance of AI ethics may differ, there is a consensus that the subject is highly relevant in the present context.

A number of studies conducted in the area of ethics in AI have been conceptual and theoretical in nature (Seah and Findlay 2021 ). Critically, there are copious numbers of guidelines on AI ethics, making it challenging for AI practitioners to decide which guidelines to follow. Unsurprisingly, studies have been conducted to analyse the ever-growing list of specific AI principles (Kelley 2021 ; Mark and Anya 2019 ; Siau and Wang 2020 ). For example, Jobin et al. ( 2019 ) reviewed 84 ethical AI principles and guidelines and concluded that only five AI ethical principles – transparency , fairness , non-maleficence , responsibility and privacy – are mainly discussed and followed. Fjeld et al. ( 2020 ) reviewed 36 AI ethical principles and reported that there are eight key themes of AI ethics – privacy , accountability , safety and security , transparency and explainability , fairness and non-discrimination , human control of technology , professional responsibility , and promotion of human values . Likewise, Hagendorff ( 2020 ) analysed and compared 22 AI ethical guidelines to examine their implementation in the practice of research, development, and application of AI systems. Some review studies focused on exploring the challenges and potential solutions in the area of ethics in AI, for example, Jameel et al. ( 2020 ); Khan et al. ( 2022 ). The desire to set ethical guidelines in AI has been enhanced due to increased competition between organisations to develop robust AI tools (Vainio-Pekka 2020 ). Among them, only a few guidelines indicate an oversight or enforcement mechanism (Inv 2019 ). It suggests that recent research has dedicated significant attention to the analysis and comparison of various sets of ethical principles and guidelines for AI.

Similarly, AI practitioners have expressed various concerns regarding the public policies and ethical guidelines related to AI. For example, while the ACM Codes of Ethics puts responsibilities to AI practitioners creating AI-based systems, a research study revealed that these practitioners generally believe that only physical harm caused by AI systems is crucial and should be taken into account (Veale et al. 2018 ). Similarly, in November 2021, the UN Educational, Scientific, and Cultural Organisation (UNESCO) signed a historic agreement outlining shared values needed to ensure the development of Responsible AI (UN 2021 ). The study conducted by Varanasi and Goyal ( 2023 ) involved interviewing 23 AI practitioners from 10 organisations to investigate the challenges they encounter when collaborating on Responsible AI (RAI) principles defined by UNESCO. The findings revealed that practitioners felt overwhelmed by the responsibility of adhering to specific RAI principles (non-maleficence, trustworthiness, privacy, equity, transparency, and explainability), leading to an uneven distribution of their workload. Moreover, implementing certain RAI principles (accuracy, diversity, fairness, privacy, and interoperability) in real-world scenarios proved difficult due to conflicts with personal and team values. Similarly, a study by Rothenberger et al. ( 2019 ) conducted an empirical study with AI experts to evaluate several AI ethics guidelines among which Microsoft AI Ethical Principles were one of them. The study found that the participants considered ‘Responsibility’ to be the foremost and notably significant ethical principle in the realm of AI. Following closely, they ranked ‘Privacy protection’ as the second most crucial principle among all other principles. This emphasises the perspective of these AI experts, who consider prioritising responsible AI practices and safeguarding user privacy to be fundamental aspects of ethical advancement and implementation of AI, without regarding other principles as equally crucial. Likewise, an empirical investigation was carried out by Sanderson et al. ( 2023 ), involving AI practitioners and designers. This study aimed to assess the Australian Government’s high-level AI principles and investigate how these ethical guidelines were understood and applied by AI practitioners and designers within their professional contexts. The results indicated that implementing certain AI ethical principles, such as those related to ‘Privacy and security’ , ‘Transparency’ and ‘Explainability’ , and ‘Accuracy’ , posed significant challenges for them. This suggests that there have been studies exploring the relationship between AI practitioners and the guidelines established by public organisations, as well as their sentiments towards each guideline.

Another prominent area of focus has been studies that were conducted to discuss the existing gap between research and practice in the field of ethics in AI. Smith et al. ( 2020 ) conducted a review study to identify gaps in ethics research and practice of ethical data-driven software development and highlighted how ethics can be integrated into the development of modern software. Similarly, Shneiderman ( 2020 ) provided 15 recommendations to bridge the gap between ethical principles of AI and practical steps for ethical governance. Likewise, there are solution-based papers and papers discussing models, frameworks, and methods for AI developers to enhance their AI ethics implementation. For example, an article by Vakkuri et al. ( 2021 ) presents the AI maturity model for AI software. In contrast, another article by Vakkuri et al. ( 2020a ) discusses the ECCOLA method for implementing ethically aligned AI systems. There are also papers presenting the toolkit to address fairness in ML algorithms (Castelnovo et al. 2020 ) and transparency model to design transparent AI systems (Felzmann et al. 2020 ). In general, it suggests that recent studies have centered on addressing the gap between research and practical application in the field of AI ethics. This also involves the development of various tools and methods aimed at improving the ethical implementation of AI.

Overall, existing studies seem to primarily focus on either analysing the plethora of ethical AI principles, filling the gap between research and practice, or discussing tool-kits and methods. However, compared to the number of papers on AI ethics describing ethical guidelines and principles, and tools and methods, there is a relative lack of studies that focus on the views and experiences of AI practitioners on AI ethics (Vakkuri et al. 2020b ). Furthermore, the literature also underscores the necessity for review studies that evaluate and synthesise the existing primary research on AI practitioners’ views and experiences of AI ethics (Khan et al. 2022 ; Leikas et al. 2019 ). To assimilate, analyse, and present the empirical evidence spread across the literature, we conducted a Grounded Theory Literature Review (GTLR) to investigate AI practitioners’ viewpoints on ethics in AI with some adaptations to the original framework, drawing data from papers whose prime focus may not have been understanding practitioners’ viewpoints but that nonetheless contained information about the same.

3 Review Methodology

While the importance of understanding AI practitioners’ viewpoints on ethics in AI has been highlighted (Vakkuri et al. 2020b ), yet, there are not enough dedicated research articles on the topic to effectively conduct a systematic literature review or mapping study. This is mainly because there are not enough papers dedicated to investigating AI practitioners’ views on ethics in AI such that their focus could be apparent from the title and abstract. Papers that include this as part of their findings are difficult to identify and select without a full read-through, making it ineffective and impractical when dealing with thousands of papers. At the same time, we were aware of a more responsive yet systematic method for reviewing the literature, called grounded theory literature review (GTLR) introduced by Wolfswinkel et al. ( 2013 ). GT is a popular research method that offers a pragmatic and adaptable approach for interpreting complex social phenomena, (Charmaz 2000 ). It provides a robust intellectual rationale for employing qualitative research to develop theoretical analyses (Goulding 1998 ). In Grounded Theory, researchers refrain from starting with preconceived hypotheses or theories to validate or invalidate. Instead, they initiate the research process by gathering data within the context, conducting simultaneous analysis, and subsequently formulating hypotheses (Strauss and Corbin 1990 ). This method is appropriate for our study because our research topic incorporates socio-technical aspects, and we also chose not to commence with a predetermined hypothesis. Instead, our approach was centered on examining the viewpoints of AI practitioners regarding AI ethics as outlined in the existing literature.

While the overarching review framework of grounded theory literature review (GTLR) helped frame the review process, we found ourselves having to work through the concrete application details using the practices of socio-technical grounded theory (STGT). In doing so, we made some adaptations to the five-step framework of define , search , select , analyse , and present described in the original grounded theory literature review (GTLR) guidelines by Wolfswinkel et al. ( 2013 ) and applied socio-technical grounded theory (STGT)’s concrete data analysis steps (Hoda 2021 ). Figure 1 presents an overview of the grounded theory literature review (GTLR) steps using the socio-technical grounded theory (STGT) method for data analysis as applied in this study. Table 1 presents the comparison between Grounded Theory Literature Review (GTLR) as we applied it, and traditional Systematic Literature Review (SLR) (Kitchenham et al. 2009 ).

Steps of the Grounded Theory Literature Review (GTLR) method with Socio-Technical Grounded Theory (STGT) for data analysis

The first step of grounded theory literature review (GTLR) is to formulate the initial review protocol, including determining the scope of the study by defining inclusion and exclusion criteria and search items, followed by finalising databases and search strings, with the aim of obtaining as many relevant primary empirical studies as possible. Studies that are empirical were one of the inclusion criteria of our study which is presented in Table 3 . By ‘empirical papers’, we are referring to those that draw information directly from primary sources, such as interviews and survey papers (studies that involve participants by using surveys to gather their perspectives on a specific subject, not literature surveys.) The research question (RQ) formulated was, What do we know from the literature about the AI practitioners’ views and experiences of ethics in AI?

3.1.1 Sources

Four popular digital databases, namely, ACM Digital Library (ACM DL), IEEE Xplore, SpringerLink, and Wiley Online Library (Wiley OL) were used as sources to identify the relevant literature. This choice was driven by the interdisciplinary nature of the topic, ‘ethics in AI.’ Given the rapid expansion of literature on AI ethics in recent years, researchers have been contributing their work to different venues. We were interested in understanding how AI practitioners perceive AI ethics. This emphasis on AI ethics perspectives was particularly prominent within Software Engineering and Information Systems venues. These databases have also been regularly used to conduct reviews on human aspects of software engineering, for example, Hidellaarachchi et al. ( 2021 ); Perera et al. ( 2020 ). Initially, we searched for relevant studies which were published in journals and conferences only and for which full texts were available.

3.1.2 Search Strings

To begin with, we initiated the process of developing search queries by selecting key terms related to our research topic. Our initial set of key terms included “ethics”, “AI”, and “developer”. This choice was made in line with the primary objective of our study, which was to investigate the perspectives of AI practitioners on ethics in AI. Subsequently, we expanded our search by incorporating synonyms for these key terms to ensure a more comprehensive retrieval of relevant primary studies. As we constructed the final search string, we employed Boolean operators ‘AND’ and ‘OR’ to link these search terms. However, using the terms “ethics”, “AI”, and “developer”, along with their synonyms, resulted in a large number of papers that proved impractical to review, as illustrated in Appendix B . In an attempt to reduce the number of papers to a manageable level, we used the term “ethic*” along with synonyms for “AI” and “developer”. Unfortunately, this approach yielded no results in some databases, as detailed in Appendix B . Therefore, it became imperative for us to develop a search query that would provide us with a reasonable number of relevant primary studies to effectively conduct our study.

Six candidate search strings were developed and executed on databases before one was finalised. Table 2 shows the initial and final search strings. As the finalised search string returned an extremely large number of primary studies (N=9,899), we restricted the publication period from January 2010 to September 2022, in all four databases, as the topic of ethics in AI has been gaining rapid prominence in the last ten years. Table 3 shows the seed and final protocols, including inclusion and exclusion criteria (Wolfswinkel et al. 2013 ).

We performed the search using our seed review protocol , presented in Table 3 . The search process was iterative and time-consuming because some combinations of search strings resulted in too many papers that were unmanageable to go through, whereas some combinations resulted in very few studies. Appendix B contains the documentation of the search process showing the revision of the first search string through to the final search string.

We obtained a total of 1,337 primary articles ( ACM DL: 312, IEEEX: 367, SpringerLink: 575 and Wiley OL: 83 ) using the final search string (as shown in Table 2 ) and the seed review protocol (as shown in Table 3 ). After filtering out the duplicates, we were left with 1073 articles. As per Wolfswinkel et al. ( 2013 ) grounded theory literature review (GTLR) guidelines, the next step was to refine the whole sample based on the title and abstract. We tried this approach for the first 200 articles each that came up in ACM DL, IEEEX, and SpringerLink and all 83 articles in Wiley OL to get a sense of the number of relevant articles to our research question. We read the abstracts of the articles whose titles seemed relevant to our research topic and tried to apply the inclusion and exclusion criteria to select the relevant articles. We quickly realised that selection based on title and abstract was not working well. This is because the presence of the key search terms (for example, “ ethics ” AND “ AI ” AND “ developer ”) was rather common and did not imply that the paper would include the practitioner’s perspective on ethics in AI. We found ourselves having to scan through full texts to judge the relevance to our research question (RQ). Despite the effort involved, the return on investment was very low, for example, for every hundred papers read, we found only one or two relevant papers, i.e., those that included the AI practitioners’ views on ethics in AI.

Out of 683 papers, we obtained only 13 primary articles that were relevant to our research topic. Many articles, albeit interesting, did not present the AI practitioners’ views on ethics in AI. So, we decided to find more relevant articles through snowballing of articles. “Snowballing refers to using the reference list of a paper or the citations to the paper to identify additional papers” (Wohlin 2014 ). Snowballing of those 13 articles via forward citations and backward citations was done to find more relevant articles and enrich the overview review quality. Snowballing seemed to work better for us than the traditional search approach. We modified the seed review protocol accordingly, to include papers published in other databases and those published beyond journals and conferences, including students’ theses, reports, and research papers uploaded to arXiv . The final review protocol used in this study is presented in Table 3 . In this way, we obtained 25 more relevant articles through snowballing, taking the total number of primary articles to 38.

Here we note that the select step of scanning through the full contents of 683 articles was very tedious with a very low return on investment, with only 13 relevant studies obtained. In hindsight, we would have done better to start with a set of seed papers that were collectively known to the research team or those obtained from some quick searches on Google Scholar. What we did next by proceeding from the seed papers to cycles of snowballing, was more practical, productive, and in line with the iterative Grounded Theory (GT) approach as a form of applied theoretical sampling.

3.4 Analyse

Our review topic and domain lent themselves well to the socio-technical research context supported by socio-technical grounded theory (STGT) where our domain was AI, the actors were AI practitioners, the researcher team was collectively well versed in qualitative research and the AI domain, and the data was collected from relevant sources (Hoda 2021 ). We applied procedures of open coding , constant comparison , and memoing in the basic stage and targeted data collection and analysis, and theoretical structuring in the advanced stage of theory development using the emergent mode.

The qualitative data included findings covered in the primary studies, including excerpts of raw underlying empirical data contained in the papers. Data were analysed iteratively in small batches. At first, we analysed the qualitative data of 13 articles that were obtained in the initial phase. We used the standard socio-technical grounded theory (STGT) data analysis techniques such as open coding, constant comparison, and memoing for those 13 articles, and advanced techniques such as targeted coding on the remaining 25 articles, followed by theoretical structuring. This approach of data analysis is rigorous and helped us to obtain multidimensional results that were original, relevant, and dense, as evidenced by the depth of the categories and underlying concepts (presented in Section 5 ). The techniques of the socio-technical grounded theory (STGT) data analysis are explained in the following section. We also obtained layered understanding and reflections through reflective practices like memo writing (Fig. 2 ), which are presented in Section 6 .

3.4.1 The Basic Stage

We performed open coding to generate codes from the qualitative data of the initial set of 13 articles. Open coding was done for each line of the ‘Findings’ sections of the included articles to ensure we did not miss any information and insights related to our research question (RQ). The amount of qualitative data varied from article to article. For example: some articles had in-depth and long ‘Findings’ sections whereas some had short sections. Open coding for some articles consumed a lot of time and led to hundreds of codes whereas a limited number of codes were generated for some other articles (Fig. 3 ).

Similar codes were grouped into concepts and similar concepts into categories using constant comparison. Examples of the application of Socio-Technical Grounded Theory (STGT)’s data analysis techniques to generate codes, concepts, and categories are shown in Fig. 3 , and a number of quotations from the original papers are included in Section 5 , to provide “ strength of evidence ” (Hoda 2021 ). The process of developing concepts and categories was iterative. As we read more papers, we refined the emerging concepts and categories based on the new insights obtained. The coding process was initiated by the first author using Google Docs initially, and later, they transitioned to Google Spreadsheet due to the growing number of codes and concepts. Subsequently, the second author conducted a review of the codes and concepts generated by the first author independently. Following this review, feedback and revisions were discussed in detail during meetings involving all the authors. To clarify roles, the first author handled the coding, the second author offered feedback on the codes, concepts, and categories, while the remaining two authors contributed to refining the findings through critical questioning and feedback.

Each code was numbered as C1, C2, C3 and labeled with the paper ID (for example, G1, G2, G3) that it belonged to, to enable tracing and improve retrospective comprehension of the underlying contexts.

While the open coding led to valuable results in the form of codes, concepts, and categories, memoing helped us reflect on the insights related to the most prominent codes, concepts, and emerging categories. We also wrote reflective memos to document our reflections on the process of performing a grounded theory literature review (GTLR). These insights and reflections are presented in Section 6 . An example of a memo created for this study is presented in Fig. 2 .

Example of a memo arising from the code (“principles vs practice gap”) labeled [C1]

3.4.2 The Advanced Stage

The codes and concepts generated from open coding in the basic stage led to the emergence of five categories: practitioner awareness , practitioner perception , practitioner need , practitioner challenge and practitioner approach to AI ethics, with different level of details and depth underlying each. Once these categories were generated, we proceeded to identify new papers using forward and backward snowballing in the advanced stage of theory development. Since our topic under investigation was rather broad, to begin with, and some key categories of varying strengths had been identified, an emergent mode of theory development seemed appropriate for the advanced stage (Hoda 2021 ).

We proceeded to iteratively perform targeted data collection and analysis on more papers. Targeted coding involves generating codes that are relevant to the concepts and categories emerging from the basic stage (Hoda 2021 ). Reflections captured through memoing and snowballing served as an application of theoretical sampling when dealing with published literature, similar to how it is applied in primary socio-technical grounded theory (STGT) studies.

We performed targeted coding in chunks of two to three sentences or short paragraphs that seemed relevant to our emergent findings, instead of the line-by-line coding, and continued with constant comparison. This process was a lot faster than open coding. The codes developed using targeted coding were placed under relevant concepts, and new concepts were aligned with existing categories in the same Google spreadsheet. In this stage, our memos became more advanced in the sense that they helped identify relationships between the concepts and develop a taxonomy. We continued with targeted data collection and analysis until all 38 selected articles were analysed. Finally, theoretical structuring was applied. This involved considering our findings against common theory templates to identify if any naturally fit. In doing so, we realised that the five categories together describe the main facets of how AI practitioners view ethics in AI, forming a form of multi-faceted taxonomy, similar to Madampe et al. ( 2021 ).

3.5 Present

As the final step of the grounded theory literature review (GTLR) method, we present the findings of our review study, the five key categories that together form the multi-faceted taxonomy with underlying concepts and codes. We developed a taxonomy instead of a theory because we adhered to the principles outlined by Wolfswinkel et al. ( 2013 ) for conducting our Grounded Theory Literature Review and according to Wolfswinkel et al. ( 2013 ), the key idea is to use the knowledge you’ve gained through analysis to decide how to best structure and present your findings in a way that makes sense and communicates your insights effectively. Likewise, we used the Socio-Technical Grounded Theory (STGT) method (Hoda 2021 ) to analyse our data, which includes a recommendation: “STGT suggests that researchers should engage in theoretical structuring by identifying the type of theories that align best with their data, such as process , taxonomy , degree, or strategies (Glaser 1978 ).” This is why we chose to create a taxonomy, as it was the most suitable approach based on the data we collected.

This is followed by a discussion of the findings and recommendations. In presenting the findings, we also make use of visualisations (see Figs. 4 and 5 ) (Wolfswinkel et al. 2013 ).

4 Challenges, Threats and Limitations

We now discuss some of the challenges, threats, and limitations of the Grounded Theory Literature Review (GTLR) method in our study.

4.1 Grounded Theory Literature Review (GTLR) Nature

Unlike a Systematic Literature Review (SLR), a Grounded Theory Literature Review (GTLR) study does not aim to achieve completeness. Rather, it focuses on capturing the ‘lay of the land’ by identifying the key aspects of the topic and presenting rich explanations and nuanced insights. As such, while the process of a grounded theory literature review (GTLR) can be replicated, the results – the resulting descriptive findings – are not easily reproducible. Similarly, our study does not aim to be exhaustive, as it adheres to a grounded theory methodology. The chosen literature sample underwent thoughtful consideration, and although it is not all-encompassing, we have taken steps to assess its representativeness. Instead of using a representative sampling approach, we used theoretical sampling in our study, acknowledging that our sample might not exhibit the same level of representativeness as seen in a Systematic Literature Review (SLR), which is one of the limitations of our study.

4.2 Search Items and Strategies

Our search and selection steps for identifying the seed papers and subsequent snowballing may have resulted in missing some relevant papers. This threat is dependent on the list of keywords selected for the study and the limitations of the search engines. To minimise the risk of this threat, we used an iterative approach to develop the search strings for the study. Initially, we chose the key terms from our research title and added their synonyms to develop the final search strings which returned the most relevant studies. For example, we included “fairness” in our final search string because when we used only the term “ethics”, we obtained zero articles in two databases ( ACM DL and Wiley OL ). The documentation of the search process is presented in Appendix B . Likewise, we only used the term “fairness” but did not include other terms like “explainability” and “interpretability” in our final search string. Due to this, there is a possibility that we missed papers that explore AI practitioners’ views on these terms (“interpretability” and “explainability”), which is a limitation of our study.

The final search terms (“ethic*” OR “moral*” OR “fairness”) AND (“artificial intelligence” OR “AI” OR “machine learning” OR “data science”) AND (“software developer” OR “software practitioner” OR “programmer”) that we used in our study seem to be biased towards engineering/computer science publication outputs. This represents one of the limitations of our research since publications related to understanding AI practitioners’ perspectives on ‘ethics in AI’ may not exclusively reside within technical publications but may also extend to disciplines within the social sciences and humanities. Our use of these search terms, which are inclined towards outputs in engineering and computer science, might have led to the omission of relevant publications from social science and humanities domains.

In our final search query, we opted for the term “software developer”. Given the iterative nature of our keyword design process, we had previously experimented with incorporating keywords like “data scientist”, in combination with terms like “AI practitioner” and “machine learning engineer”, to ensure that we did not inadvertently miss relevant papers. Unfortunately, this led to an overwhelming number of papers, posing a challenge for our study. Therefore, we decided to reduce the number of keywords and used only terms like “software developer”, “software practitioner”, and “programmer” to obtain a more manageable set of papers for our study. However, we acknowledge that not including the term “data scientist” in the search query may have caused us to miss some relevant papers, which is a limitation of our study.

The main objective of our study was to explore the empirical studies that focused on understanding AI practitioners’ views and experiences on ethics in AI. We were looking at the people involved in the technical development of AI systems but not managers, which is a limitation of our study. However, future studies could encompass managers, or separate reviews may delve into their perspectives on AI ethics. Likewise, we focused on studies published in the Software Engineering and Information Systems domains. However, we acknowledge that AI practitioners’ perspectives on AI ethics might have been extensively studied in social sciences and humanities, areas we didn’t explore - a limitation of our study. Future research can encompass studies from these domains.

4.3 Review Protocol Modification

We decided to include only research-based articles in our grounded theory literature review (GTLR) study. Future grounded theory literature review (GTLR) studies can include literature from non-academic sources like in multi-vocal literature reviews (MLRs). Since, there is a lack of theories, frameworks, and theoretical models around this topic, we wanted to conduct a rigorous review study to present multidimensional findings and develop theoretical foundations for this critical and emerging topic. Finding enough empirical articles related to the research topic was another challenge. To overcome this, we had to make some adaptions to the original grounded theory literature review (GTLR) framework proposed by Wolfswinkel et al. ( 2013 ) and relaxed the review protocol during the snowballing of articles and included studies published in venues other than journals and conferences. We also used studies uploaded on arXiv as our seed papers due to the lack of enough peer-reviewed publications relevant to our research topic. arXiv is a useful resource to find the latest research on emerging topics, and the quality of the work can be reasonably assessed from the draft. The growing impact of open sources like arXiv is evidenced by the increase in direct citations to arXiv in Scopus-indexed scholarly publications from 2000 to 2013 (Li et al. 2015 ).

Taxonomy of ethics in AI from practitioners’ viewpoints

4.4 Time Constraints

We applied the socio-technical grounded theory (Hoda 2021 ) approach to analyse the qualitative data of primary studies and focused on the ‘Findings’ section of the studies that presented empirical evidence. We did not find information on tools/software/framework/models used by AI practitioners to implement ethics in AI, although a study mentioned the existence of various tools but with no details provided [G10]. Since we were following a broad and inductive approach, we were not specifically looking for information on tools. This lack of information was surprising, but future reviews and studies can investigate the use of tools in implementing AI ethics.

As explained above, five key categories emerged from the analysis: (i) practitioner awareness , (ii) practitioner perception , (iii) practitioner need , (iv) practitioner challenge and (v) practitioner approach . Taken together, they form a taxonomy of ethics in AI from practitioners’ viewpoints , shown in Fig. 4 , with the underlying codes and concepts. Taken together, they represent the key aspects AI practitioners have been concerned with when considering ethics in AI. We describe each of the five key categories, and their underlying codes and concepts, and share quotes from the included primary studies by attributing them to paper IDs, G1 to G38 . The list of included studies is presented in Appendix A .

5.1 Practitioner Awareness

The first key category, or facet of the taxonomy, that emerged is Practitioner Awareness . This category emerged from two underlying concepts: AI ethics & principles-related awareness and team-related awareness .

5.1.1 AI Ethics & Principles—Related Awareness

5.1.2 Team—Related Awareness

Participants in some studies acknowledged their awareness of their roles and responsibilities in integrating ethics into AI during its development. For instance, a participant from the study [G7] highlighted being aware of their roles and responsibilities in implementing ethics during the development of AI systems. Similarly, a participant in another study [G23] expressed awareness of playing a pivotal role in shaping the ethics embedded in an AI system.

In a study [G25], a participant acknowledged their lack of knowledge about ethics of AI. Similarly, participants in other studies, such as [G6] and [G8], also expressed awareness of their insufficient understanding of AI ethics and ethical principles.

5.1.3 Overall Summary

Few AI practitioners reported their awareness of the concept of AI ethics, ethical principles, their importance, and relevance in AI development. Likewise, very few AI practitioners were aware of the gap that exists between the ethical principles of AI and practice. Overall, this indicates a positive aspect concerning AI practitioners, as awareness of ethics is the initial step toward implementing ethical practices in AI development.

Similarly, some AI practitioners reported their understanding of the roles and responsibilities involved in the development of ethical AI systems. However, the primary focus of the majority of AI practitioners who participated in some studies was on recognising their own limitations that could result in the development of unethical AI systems. These limitations encompassed a lack of foresight and intention, insufficient self-reflection, limited knowledge of ethics, and a lack of awareness regarding cultural norms. In summary, this suggests that AI practitioners who participated in those studies engaged in significant introspection to comprehend the reasons behind the development of unethical AI systems. This introspective approach is positive because self-reflection can play a crucial role in identifying personal shortcomings and finding ways to address them.

5.2 Practitioner Perception

The second category is Practitioner Perception which emerged from four underlying concepts: AI ethics & principles-related perception , User-related perception , Data-related perception , and AI system-related perception .

The perception category goes beyond acknowledging the existence of something and captures practitioners’ views & opinions about it, including held notions and beliefs. For example, it includes shared perceptions about the relative importance of ethical principles in developing AI systems, who is considered accountable for applying and upholding them, and the perceived cost of implementing ethics in AI.

5.2.1 AI Ethics & Principles-Related Perception

Perceptions about the importance of ethics varied. Some AI practitioners who participated in studies like [G1], [G29], and [G20] perceived ‘ethics’ as very important in developing AI systems. A study [G1] reported that AI practitioners acknowledged the importance of AI ethics. In the paper, when participants were asked if ethics is useful in AI, all (N=6) of them answered “Yes”. Nevertheless, it’s important to consider that the participant sample size of this study [G1] was only 6.

Developing responsible AI was seen as building positive relations between organisations and human beings by minimising inequality, improving well-being, and ensuring data protection and privacy. However, when it comes to the relative importance of ethical principles, it was a divided house. An AI practitioner who participated in a study [G11] thought that AI systems must be fair in every way. Likewise, some participants in another study [G8] also thought that fairness issues in AI systems must not only be minimised but completely avoided, highlighting the importance of developing a fair AI system. On the other hand, within the same study [G8] surveying 51 participants, the highest importance, with an arithmetic mean of 4.71, was attributed to the principle of Protection of data privacy. Other studies – [G6] and [G10] – also concluded that ‘Privacy protection and security’ was the most important ethical principle in AI system development.

5.2.2 User-Related Perception

Some AI practitioners who participated in studies like [G2], [G3], [G5], [G6], [G7], and [G34] had perceptions about users’ nature, technical abilities, drivers, and their role in the context of ethics in AI. In this context, “users” encompassed either the party commissioning a system, the end users, or both. We have provided additional clarity regarding the specific user categories that participants referred to when engaging in discussions about ethics in AI.

5.2.3 Data-Related Perception

The developer’s naïve perception of the potential for harm (or lack thereof) is worth noting in the above example. Along with that, some participants in a study [G2] highlighted the importance of data collection and curation in AI system development. They mentioned that collecting sufficient data from sub-populations and balancing them during the curation of data sets is essential to minimising the ethical issues of an AI system. A participant in [G15] also shared a similar idea on collecting sufficient ethical data for developing AI systems.

On the other hand, some participants in a study [G18] reported that they minimised getting the personal data of users or avoided its collection as much as possible so that no ethical issues related to data privacy arise during AI system development, whereas a participant in [G21] mentioned that they used privacy-preserving data collection techniques to reduce unethical work with data.

5.2.4 AI System-Related Perception

5.2.5 Overall Summary

Overall, our synthesis says that AI practitioners who participated in the studies had both positive and negative perceptions about the concept of AI ethics. While some practitioners thought ethics were important to consider while developing AI systems, others perceived it as a secondary concern and non-functional requirement of AI. This diversity of views on AI ethics can have implications for the development and deployment of AI technologies and how ethical considerations are integrated into AI practices. Likewise, there were different views on the importance of different principles of AI ethics. Some practitioners perceived developing a fair AI is important whereas others perceived maintaining privacy during AI development is more important. This diversity in the views of different ethical principles might also impact the development of ethical AI-based systems.

Perceptions regarding ethical considerations in the development of AI systems also extended to the question of responsibility. While some AI professionals felt it was their duty to create ethical AI systems and bear the accountability for any resulting harm, others believed that both users and practitioners shared this responsibility. We think it’s essential to establish clear definitions of who should be accountable for ethical considerations during AI development and the consequences that arise from it. This way, there can be no evasion of this important issue. The discussion revolved around the expense associated with implementing ethical standards in AI development. We are curious whether, in the absence of cost barriers, AI practitioners could have created more ethically sound AI systems.

Some practitioners who participated in the studies also held unfavorable views regarding AI system users. Some believed that users generally did not pay much attention to AI ethics until actual ethical problems arose. Users were viewed as making judgments about AI systems based on personal biases rather than a deep understanding of how AI worked. Additionally, some participants perceived that users might resort to legal action against companies only when ethical issues with AI systems become apparent. Overall, this suggests a gap in user awareness and engagement with AI ethics, which could have implications for how AI is developed, used, and regulated.

Likewise, AI practitioners perceived a few steps to be important related to data to develop ethical AI systems. Proper data handling, sufficient data collection and data balancing, and avoiding personal data collection were perceived as important measures to mitigate ethical issues of AI systems. This implies that data-related practices contribute to ethical behavior and responsible AI development.

A few AI practitioners also had mixed perceptions about the nature of AI systems. Some expressed pessimism, suggesting that AI systems are excessively complex and inherently possess ethical issues that are difficult to mitigate. On the other hand, others viewed AI as socio-technical systems that, at the very least, take ethical considerations into account. Overall, this diversity in views highlights the ongoing debate and complexity surrounding AI ethics and underscores the importance of continued discussion and efforts to improve the ethical aspects of AI technology.

5.3 Practitioner Need

The review highlighted the different needs of AI practitioners which can help them enhance ethical implementation in AI systems. This category is underpinned by concepts such as AI ethics & principles-related need and team-related need .

5.3.1 AI Ethics & Principles—Related Need

Likewise, a few AI practitioners who participated in a study [G1] and [G5] reported that they are challenged to implement ethics in AI as there is a lack of tools or methods for implementing ethics . For example, in a study [G1] , when AI practitioners were asked, “Do your AI development practices take into account ethics, and if yes, how?” , all respondents (N=6) answered “No” . This indicates that AI companies lack clear tools and methods that help AI practitioners implement ethics in AI. Another study [G19] concluded that there is a lack of tools that support continuous assurance of AI ethics. A participant in a study [G19] stated that it was challenging for them as they had to rely on manual practice to manage ethics principles during AI system development.

5.3.2 Team—Related Need

There are a few needs related to AI practitioners that influence ethical implementation in a system. There is a need for effective communication between AI practitioners as it supports ethics implementation [G2], [G3], [G15]. A few participants in studies [G2] and [G3] expressed the need for tools to facilitate communication between AI model developers and data collectors. In the study [G2], out of those surveyed, 52% of respondents (79% of them when asked) expressed that tools aiding communication between model developers and data collectors would be incredibly valuable.

Similarly, some participants in [G18] reported that they were technology experts but didn’t have any knowledge and background in ethics . However, they were extremely aware of privacy concerns in AI use, highlighting an interesting relationship between practitioner awareness, perception, and challenges. A few participants in other studies like [G6], [G8], and [G25] also supported the notion.

5.3.3 Overall Summary

The AI practitioners who participated in the included primary studies discussed several requirements concerning the conceptualisation of AI ethics and ethical guidelines. Some of them also expressed the necessity for tools and methodologies that could aid them in improving the development of ethical AI systems. This suggests that there is an ongoing need for support and resources to assist AI practitioners in adhering to ethical principles during the AI development process.

Similarly, a few participants in some of the included primary studies also addressed certain requirements regarding AI development teams. Some of these needs pertained to individual self-improvement, including the improvement of communication within the team and possessing a strong foundation in ethics as prerequisites for developing ethical AI systems. Additionally, there was a mention of the importance of discussing ethical responsibilities among team members as another requirement. Overall, the data suggests a commitment to improving the ethical aspects of AI development, both in terms of principles and practical implementation, and a recognition that addressing these ethical challenges requires a multifaceted approach involving teams and individual professionals.

5.4 Practitioner Challenge

The fourth key category is Practitioner Challenge . Several challenges are faced by AI practitioners in implementing AI ethics including AI ethics and principles-related challenge , organisation-related challenge , AI system-related challenge , and data-related challenge .

5.4.1 AI Ethics & Principles—Related Challenge

A number of challenges related to implementing AI ethics were reported, including knowledge gaps, gaps between principles & practice, ethical trade-offs including business value considerations, and challenges to do with implementing specific ethical principles such as transparency, privacy, and accountability.

Different types of challenges are mentioned and solutions are discussed in theory but there is no demonstration of those solutions in practice [G1], [G3]. Translation of AI principles into practice is a challenge for AI practitioners as discussed by some participants in studies including [G1] and [G6].

5.4.2 Organisation—Related Challenge

5.4.3 AI System—Related Challenge

5.4.4 Data—Related Challenge

5.4.5 Overall Summary

Participants in the included primary studies discussed various challenges related to the concept of AI ethics and ethical principles. Some participants discussed challenges related to ethics, including variations in how people understand ethics, the practical application of ethical principles, and the consistent adherence to various ethical standards throughout the AI development process. In general, this data suggests that the primary challenge for practitioners is grasping the essence of ethics, which we consider to be the fundamental issue and should be prioritised for resolution.

Similarly, organisations have contributed to obstructing AI practitioners in their efforts to develop ethical AI systems. Challenges raised by participants, such as limited budgets for integrating ethics, tight project deadlines, and restricted decision-making authority during AI development, indicate that organisations could assist AI practitioners by addressing these issues when feasible.

Some participants also discussed the challenges regarding the unpredictability of AI systems. They identified factors contributing to this unpredictability, such as profit maximisation, attention optimisation, and cyber-security threats. The absence of contingency plans to address issues stemming from AI system unpredictability was also discussed. Overall, it indicates that AI practitioners employ certain strategies to mitigate unpredictability in AI systems, but there is a demand for methods and tools to effectively prevent or manage such unpredictability. The development of such methods or tools would aid in reducing ethical risks associated with AI.

Participants discussed challenges associated with the data used to train AI models. They explained how the quality of data and the processes involved in handling data can influence AI development. Some AI practitioners faced challenges related to ensuring the ethical development of AI, primarily due to issues like inadequate data quality, poor data collection practices, and improper data usage. Overall, the data suggests that to ensure ethical AI development, it is essential to address issues related to data quality and data handling processes.

5.5 Practitioner Approach

The review of empirical studies provided insights into the approaches used by AI practitioners to implement ethics during AI system development. This category is underpinned by three key concepts, AI ethics & principles-related approach , team-related approach , and organisation-related approach to enhance ethics implementation in AI. AI practitioners discussed the applied and/or potential strategies related to these three concepts. Applied strategies refer to the techniques or ways that AI practitioners reported using to enhance the implementation of ethics in AI, whereas possible strategies are the recommendations or potential solutions discussed by AI practitioners to enhance the implementation of ethics in AI.

5.5.1 AI Ethics & Principles—Related Approach

AI practitioners were also involved in setting customised regulations in the company and played an essential role in the development of AI ethics. This strategy was used to enhance ethics implementation by developing comprehensive and well-defined guidelines for AI ethics for the company [G7]. Some participants in a study [G11] also reported that they needed to customize the general policies in the organisation to better support privacy and accessibility for their specific circumstances to ensure AI fairness.

5.5.2 Team—Related Approach

Some participants in a study [G1] reported that organisations used proactive strategies such as speculating socio-ethical impacts and analysing hypothetical situations to enhance ethics implementation in AI development. Likewise, a few participants in another study [G5] supported the notion and mentioned that such strategies aimed to address ethical issues that may arise and plan for their potential consequences [G5]. Analysing a hypothetical situation of unpredictability was a strategy used to solve an AI system’s unpredictable behavior [G1]. Similarly, a participant in a study [G2] reported that speculating possible fairness issues of an AI system before deploying it was a strategy used to minimise fairness issues [G2] in AI.

However, some companies did not use proactive strategies to maintain transparency of AI systems but addressed transparency issues only when it impacted their business [G6]. Some AI practitioners just followed what is legal and shifted the ethical responsibilities to policymakers and legislative authorities [G7]. In contrast, some participants in a study [G24] placed the ethical responsibility on the company manager.

In addition to sharing experiences of tried and tested strategies, practitioners also discussed potential strategies that they thought could improve ethics in AI. A study [G10] concluded that appointing one individual to implement ethics during AI development is not a good option. The whole AI development team must be involved in the process of ethics implementation. In another study [G15], a participant proposed a similar notion, emphasising the involvement of not just senior members but also junior AI practitioners in integrating ethics during AI development.

Likewise, a participant in a study [G10] mentioned that tackling ethical issues timely i.e., during the design and development of an AI system to enhance system transparency is good. In another study [G4], a participant recommended addressing ethical concerns during the development of AI systems, highlighting the necessity for providing AI developers with supportive methods.

5.5.3 Organisation—Related Approach

Some participants in a study [G18] reported several strategies provided by organisations to enhance ethics implementation in AI such as ethics review boards . Likewise, a participant in a study [G21] mentioned that having internal governance such as ethics committees in an organisation to establish AI ethical standards can provide AI practitioners an opportunity to work closely with ethicists so that they can verify if ethics is being implemented appropriately during AI system development.

Some participants in studies like [G1] and [G5] stated that conducting audits was the other important strategy organisations provided to them to solve transparency issues. A participant in [G21] reported that employing AI auditors could help AI practitioners in developing ethical AI systems.

5.5.4 Overall Summary

Participants discussed several strategies that they used to ensure the ethical development of AI systems. The applied strategies related to AI ethics and principles were used by the participants to ensure the ethical development of AI systems such as merging ethics and law and setting customised AI ethics regulations in the company. Overall, this indicates that practitioners emphasize the comprehensive integration of all AI ethical principles to ensure that no aspect is overlooked during the development process.

Some approaches were performed by the team to ensure the ethical development of AI systems such as group discussions with colleagues on AI ethics, analysing hypothetical situations of AI ethical issues, considering socio-ethical impacts of AI, and discussion with policymakers and legal teams to ensure algorithms are abiding by laws. Overall, this data suggests a comprehensive and multidisciplinary approach to addressing AI ethics, where the team actively engages in discussions, analysis, and collaboration with various stakeholders to promote the ethical development of AI systems.

Some participants mentioned that their organisations currently use various methods, such as audits, and ethics review boards, to promote ethical AI development. However, the discussion highlighted a greater emphasis on potential approaches that organisations could offer to their AI development teams to ensure ethical AI. For instance, some participants proposed that organisations could prioritise diversity within AI teams, provide education and training on AI ethics for practitioners, establish internal governance mechanisms like ethics committees, cultivate a cultural shift within the organisation towards ethical considerations, and implement tools like quizzes during the hiring process for AI teams to enhance ethical development. It indicates that organisations can offer additional support to AI practitioners in their pursuit of ethical AI systems, suggesting that there is more that can be done in this regard.

6 Discussion and Recommendations

6.1 taxonomy of ethics in ai from practitioners’ viewpoints.

The taxonomy of ethics in AI from practitioners’ viewpoints aims to assist AI practitioners in identifying different aspects related to ethics in AI such as their awareness of ethics in AI, their perception towards it, the challenges they face during ethics implementation in AI, their needs, and the approaches they use to enhance better implementation of ethics in AI. Using the findings, we believe that AI development teams will have a better understanding of AI ethics, and AI managers will be able to better manage their teams by understanding the needs and challenges of their team members.

An overview of the taxonomy and the coverage of the underlying concepts across the categories is presented in Fig. 5 . As mentioned previously, we obtained multiple concepts for each category. Some concepts were common across some categories whereas some were unique. For example, ‘AI ethics & principles’ is a concept that emerged for each of the five categories, depicted by a full circle around the five categories. The ‘ teams-related ’ concept emerged for three categories, namely, practitioner awareness , practitioner need , and practitioner approach , depicted by a crescent that covers these three categories on the top left. While the ‘ user-related ’ concept emerged for only one category, practitioner perception , as seen by a small crescent over that category. The codes underlying these concepts were unique to each category, as seen in Fig. 4 and described in the ‘Findings’ section.

An overview of the aspects of ethics in AI from AI practitioners’ viewpoints

The overview of the taxonomy shows that AI practitioners are mostly concerned about AI ethics and ethical principles. For example, they discussed their awareness of ethics [G16] and different AI ethical principles such as transparency [G17], accountability [G3], fairness [G2], and privacy [G6] and also shared their positive perception such as its importance and benefits, and negative perceptions such as the high cost of ethics application [G6] and ethics being a non-functional requirement in AI development [G10]. Likewise, they mentioned different challenges they faced during AI ethics implementation which are related to AI ethics and principles such as ethics conceptualisation [G1], the difficulty of translating principles to practice [G6] and making ethical choices [G7]. Their needs related to AI ethics and principles were also reported by AI practitioners in the literature including the need for universal ethics definition [G1], tools to translate principles to practice [G6] along with the approaches they used related to AI ethics and principles to enhance better implementation of ethics in AI such as merging ethical and legal considerations and setting customised regulations in the organisation [G7].

On the other hand, the review shows that AI practitioners have been less concerned about the aspects related to users when it comes to ethics in AI. For example: AI practitioners perceive that users are unconcerned and incurious [G5] about the ethical aspects of AI software they use unless there is any chance of an incident occurring [G3]. Likewise, they reported that users don’t have much knowledge about AI which makes them uninterested in the ethical aspects of AI-based systems [G7]. No challenges or needs related to users were reported in the literature that impact AI practitioners’ AI ethics implementation in AI-based systems. In conclusion, AI ethics and principles and team-related aspects were front and center for AI practitioners while they lacked a better view of the user-related aspects. Our findings contribute to the academic and practical discussions by exploring the studies that have included the views and experiences of AI practitioners about ethics in AI. As we conducted a grounded theory literature review (GTLR), we got an opportunity to rigorously review the primary empirical studies relevant to our research question and develop a taxonomy. We now discuss some of the insights captured through memoing and team discussions, accompanied by recommendations.

6.2 Ethics in AI – Whose Problem is it Anyway?

Participants of the primary studies had different perceptions of AI ethics and its implementation. Most studies included in our research concluded that AI practitioners perceived ethics as an essential aspect of AI [G5], [G20]. However, some participants had other viewpoints. A participant in [G1] stated that discussion on AI ethics does not affect most people, except for AI ethics discussions in massive companies like Google. Another participant from [G4] perceived ethics as a non-functional requirement in AI, something to be implemented externally [G23]. In contrast, a participant in [G4] stated that ethics could not be “outsourced”, and it should be implemented by AI practitioners who are developing the software. The diverse perspective of the participants about the implementation of ethics in AI serves to highlight the complex nature of the topic and why organisations struggle to implement AI ethics.

Likewise, there were also different views on who should be accountable for implementing ethics in AI. An AI practitioner in a study [G30] shared the uncertainty typically present when deciding who or what is responsible when ethical issues arise in AI systems. It seems certain organisations attempt to define who should be held accountable, but again, there is no universal understanding. For example: the ACM Code of Ethics clearly puts the responsibility on professionals who develop these systems. On the other hand, AI practitioners perceive that only physical harm caused by AI systems is essential and needs to be considered [G3]. This statement is alarming as it hints that some practitioners carry the view that only physical harm is worth being concerned about.

Recommendations for Practice

Given the diverse perspectives on who owns accountability for considering ethics in AI systems development and potential ethical issues arising from AI system use, it is important for AI development teams, which are usually multidisciplinary in nature, as well as managers and organisations at large to have open discussions about such issues at their workplace [G5]. The lack of discussion about ethics within the tech industry has been identified as a significant challenge by engineers (Metcalf et al. 2019 ). For example, this can be done through organising discussion panels, guest seminars by ethics and ethical AI experts, and hosting open online forums for employees to discuss such topics. Another approach is to collate the challenges specific to the organisation and see how they map to selected ethical frameworks, as was conducted at Australia’s national scientific research agency (CSIRO) [G26].

Practitioner discussions can be followed by strategic and organised attempts to reconcile perspectives, for example, teams collaboratively selecting an existing or creating a bespoke ethical framework, and drafting practical approaches to implement them in their specific project contexts [G7], many of which may be application domain specific.

We recommend proactive awareness as evidenced in our reviews, such as driven by personal interest and experiences [G6], organisational needs [G3], and regulations such as the General Data Protection Regulation (GDPR) [G6]. Whereas reactive awareness , driven by customer complaints about AI ethical issues and negative media coverage [G2], is not desirable.

Similarly, we recommend proactive strategies such as speculating socio-ethical impacts by AI practitioners prior to developing an AI system [G5]. Speculating socio-ethical impacts hints at speculative design approaches which have been heavily discussed and supported by multiple studies as well (Lee et al. 2023 ; Alfrink et al. 2023 ). Analysing hypothetical situation of unpredictability to solve unpredictable behaviour of an AI system [G1], following codes of ethics and standards of practice [G18], including diverse people in the development team [G21], and having internal governance such as ethics committees in an organisation to establish AI ethical standards [G21] are also other proactive strategies we recommend.

Finally, there is also a need to consider accountability at the organisation and industry levels. For example: Ibanez et al. (2021) [G6] reported that there is a need for ethical governance that can help them solve accountability issues.

6.3 Ethics-Critical Domains Lead the Way

Comparisons were made between the medical field and the IT field in terms of the awareness of ethical regulations in AI [G5]. Participants mentioned that practitioners developing AI used in the medical field are more aware of ethics because the medical field has stricter laws and regulations than IT. This hints that awareness of AI ethics depends on domain specificity . Domains such as medical and health are more ethics-aware than others and lead the way in ethics awareness and implementation.

The IT domain can learn from the advances in improving the awareness of and implementing ethics in the medical domain (Mittelstadt 2019 ). This includes digital, virtual, mobile, and tele-health areas, as well as AI systems developed in other domains.

Labelling certain domains as safety-critical and equating that with ethics-critical, can be a flawed argument leading to perceptions that domains traditionally considered non-safety-critical, such as gaming and social media, can be held to lower standards and expectations when it comes to ethics implementation. We know from multiple cases of cyberbullying and ‘intelligent’ games encouraging self-harm in young adults (for example, ‘The Blue Whale Game’ (Mukhra et al. 2019 )) that this would be a mistake. We recommend that all domains should aim to be ethics-critical.

6.4 Research can help in Fundamental and Practical Ways

The perspectives of AI practitioners on the nature of AI systems can have a significant impact on the implementation of ethics in AI. Some practitioners may view AI as a socio-technical system and therefore place a strong emphasis on ethics [G4], while others may view AI as a complex system and find it challenging to address ethical issues, leading them to avoid ethical considerations [G7]. The participants’ perspectives on AI systems indicate that the implementation of ethics depends on how practitioners perceive AI ethics.

Recommendations for Research

Based on our review findings, we recommend research including empirical studies, reviews, and solutions & tools development into the following topics.

Most of the participants in a study [G9] reported that there is no use of ethical tools in AI companies to enhance ethics implementation in AI. Therefore, reviewing tools available to AI practitioners to enhance the AI ethics implementation including their evaluation and feedback for improvement would be helpful to make them aware of the tools that are beneficial.

Based on our findings, it appears that some AI practitioners involved in studies such as [G5, G6, G19] mentioned the need for assistance in the form of tools and methodologies to effectively integrate ethics into AI and put ethical principles into action. Consequently, designing solutions in the form of tools and guidelines to tackle the challenges faced by them, by working in close collaboration with practitioners would be advantageous.

Investigating the users’ view of ethics in AI, for example, through a similar grounded theory literature review (GTLR) approach as applied in this review to address the practitioners’ view because to the best of our knowledge, this is the first grounded theory literature review (GTLR) in Software Engineering.

Understanding the interplay between the role of practitioners and users in implementing ethics in the development and use of AI systems as one of the findings of our study shows that AI practitioners who participated in the included primary studies were less concerned about user-related aspects when it comes to developing ethical AI systems, including human limitations, biases, and strengths.

7 Methodological Lessons Learned

We followed Wolfswinkel et al. ( 2013 ) guidelines to conduct our grounded theory literature review (GTLR) as it is an overarching review framework that helped us frame the review process. A grounded theory literature review (GTLR) is suitable for exploring new and emerging research areas deeply, building theories, and making practical recommendations. The process involves an iterative approach to finding relevant papers to the research topic. As per Wolfswinkel et al. ( 2013 ), you refine the sample based on the title and abstract after removing duplicates. However, the guidelines don’t provide clear steps if the return on investment is low. As mentioned in Section 3.3 , we read the title and abstract of the first few samples (we read 200 papers) in three databases, including ACM DL, IEEEX, and SpringerLink, and all 83 papers in Wiley OL to gauge how many papers we might get. Unfortunately, this method proved inefficient, requiring full-text scans to judge relevance to our research topic. Despite considerable effort, the return on investment was minimal, with only one or two relevant papers found that included AI practitioners’ views on ethics in AI for every hundred papers. This experience taught us that for a very new research topic with highly specific inclusion and exclusion criteria, it is not worth going through the titles and abstracts of all the papers in the initial search due to the expected low return on investment.

From our initial search, we found only 13 papers. Since, Wolfswinkel et al. ( 2013 ) welcome adaptations to their framework by acknowledging that “... one size does not fit all, and there should be no hesitation whatsoever to deviate from our proposed steps, as long as such variation is well motivated”, we conducted forward and backward snowballing on those 13 articles. During the snowballing process, we had to modify our seed review protocol to find relevant papers that had information on AI practitioners’ views on ethics in AI. This significantly helped us find more relevant articles-25 more, to be precise. We discovered that employing the forward and backward snowballing method and relaxing the review protocol after identifying seed papers is a more effective way to find relevant literature, as it worked well for our research. While Wolfswinkel et al. ( 2013 ) guidelines don’t explicitly mention adjusting the review protocol, they are open to adaptations. In our study, we embraced this flexibility and made modifications that proved successful for us.

8 Conclusion

AI systems are as ethical as the humans developing them. It is critical to understand how the humans in the trenches, the AI practitioners, view the topic of ethics in AI if we are to a lay firm theoretical foundation for future work in this area. With this in mind, we formulated the research question: What do we know from the literature about the AI practitioners’ views and experiences of ethics in AI? To address this, we conducted a grounded theory literature review (GTLR) introduced by Wolfswinkel et al. ( 2013 ), applying the concrete steps of socio-technical grounded theory (STGT) for data analysis and developed a taxonomy (Hoda 2021 ), based on 38 primary empirical studies. Since there were not many empirical studies focusing on this niche topic exclusively, a grounded theory-based iterative and responsive review approach worked well to identify and extract relevant content from across multiple studies (that mainly focused on other related topics). The application of socio-technical grounded theory (STGT) for data analysis procedures such as open coding, constant comparison, memoing, targeted coding, and theoretical structuring enabled rigorous analysis and taxonomy development. We identified five categories of practitioner awareness , practitioner perception , practitioner need , practitioner challenge , and practitioner approach , including the underlying concepts and codes giving rise to these categories. Taken together, and applying theoretical structuring, we developed a taxonomy of ethics in AI from practitioners’ viewpoints to guide AI practitioners, researchers, and educators in identifying and understanding the different aspects of AI ethics to consider and manage. The taxonomy serves as a research agenda for the community, where future work can focus on investigating and explaining each of the individual phenomena of practitioner awareness, perception, challenge, need, and approach in-depth. Future empirical studies can focus on improving the understanding and implementation of ethics in AI and recommend practical approaches to minimise ethical issues such as mitigating potential biases in AI development through frameworks and tools development.

Data Availibility

All data generated or analysed during this study are included in this published article (and its supplementary information files).

https://www.industry.gov.au/publications/australias-artificial-intelligence-ethics-framework/australias-ai-ethics-principles

Throughout the manuscript we use the term “product” for simplicity to refer to both “products and services” where the distinction is usually straightforward from context. Also, the term ‘AI development’ encompasses both the development and implementation of new and existing AI methods and the use of AI methods as a key component as part of a broader system.

The term ‘practitioners’ in our study includes AI developers, AI engineers, AI specialists, and AI experts. The terms ‘AI practitioners’ and ‘practitioners’ are used interchangeably throughout our study.

We chose the term ‘AI system’ as an overarching way of capturing both AI and ML-based systems and this is based on the fact that all these seed papers that we included in our study are focused on either AI, ML, or both.

This study uses the term ‘primary research articles’ to denote empirical works where AI practitioners were directly approached for their perspectives.

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Acknowledgements

Aastha Pant is supported by the Faculty of IT Ph.D. scholarship from Monash University. C. Tantithamthavorn is partially supported by the Australian Research Council’s Discovery Early Career Researcher Award (DECRA) funding scheme (DE200100941). Also, the authors would like to thank Prof. John Grundy for his constructive feedback on the paper.

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Appendix B: Documentation of the Search Process

Documentation of the search process

Appendix C: Glossary of Terms

In this section, we provide definitions for certain terms used in the manuscript. The definitions referenced are directly sourced, while those without citations are developed by the authors.

Ethics : The moral principles that govern the behaviors or activities of a person or a group of people (Nalini 2020 ).

AI Ethics : The principles of developing AI to interact with other AIs and humans ethically and function ethically in society (Siau and Wang 2020 ).

AI Practitioner : The term ’practitioners’ in our study includes AI developers, AI engineers, AI specialists, and AI experts. The terms ‘AI practitioners’ and ‘practitioners’ are used interchangeably throughout our study.

Fairness : AI systems should be inclusive and accessible, and should not involve or result in unfair discrimination against individuals, communities, or groups (Aus 2023 ).

Accountability : People responsible for the different phases of the AI system lifecycle should be identifiable and accountable for the outcomes of the AI systems, and human oversight of AI systems should be enabled (Aus 2023 ).

Transparency and explainability: There should be transparency and responsible disclosure so people can understand when they are being significantly impacted by AI and can find out when an AI system is engaging with them (Aus 2023 ).

Privacy protection and security : AI systems should respect and uphold privacy rights and data protection, and ensure the security of data (Aus 2023 ).

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Pant, A., Hoda, R., Tantithamthavorn, C. et al. Ethics in AI through the practitioner’s view: a grounded theory literature review. Empir Software Eng 29 , 67 (2024). https://doi.org/10.1007/s10664-024-10465-5

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DOI : https://doi.org/10.1007/s10664-024-10465-5

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KG-EmpiRE: A Community-Maintainable Knowledge Graph for a Sustainable Literature Review on the State and Evolution of Empirical Research in Requirements Engineering

In the last two decades, several researchers provided snapshots of the “current” state and evolution of empirical research in requirements engineering (RE) through literature reviews. However, these literature reviews were not sustainable, as none built on or updated previous works due to the unavailability of the extracted and analyzed data. KG-EmpiRE is a Knowledge Graph (KG) of empirical research in RE based on scientific data extracted from currently 680 papers published in the IEEE International Requirements Engineering Conference (1994-2022). KG-EmpiRE is maintained in the Open Research Knowledge Graph (ORKG), making all data openly and long-term available according to the FAIR data principles. Our long-term goal is to constantly maintain KG-EmpiRE with the research community to synthesize a comprehensive, up-to-date, and long-term available overview of the state and evolution of empirical research in RE. Besides KG-EmpiRE, we provide its analysis with all supplementary materials in a repository. This repository contains all files with instructions for replicating and (re-)using the analysis locally or via executable environments and for repeating the research approach. Since its first release based on 199 papers (2014-2022), KG-EmpiRE and its analysis have been updated twice, currently covering over 650 papers. KG-EmpiRE and its analysis demonstrate how innovative infrastructures, such as the ORKG, can be leveraged to make data from literature reviews FAIR, openly available, and maintainable for the research community in the long term. In this way, we can enable replicable, (re-)usable , and thus sustainable literature reviews to ensure the quality, reliability, and timeliness of their research results.

Index Terms:

I introduction.

For 20 years, various researchers conducted literature reviews to examine the state and evolution of empirical research in requirements engineering (RE) with the shared goal of providing a comprehensive, up-to-date, and long-term available overview  [ 1 , 2 ] . However, these literature reviews were not sustainable, as none built on or updated previous ones, which are known challenges of literature reviews  [ 3 ] . While recent research addresses these challenges by providing social and economic decision support and guidance  [ 3 ] , the underlying problem is the unavailability of the extracted and analyzed data. Researchers need technical support, i.e., infrastructures, to conduct sustainable literature reviews so that all data is openly and long-term available according to the FAIR data principles  [ 3 ] and corresponding to open science in SE  [ 4 ] .

In their joint work, Wernlein  [ 5 ] and Karras et al.  [ 1 , 6 ] examined the use of the Open Research Knowledge Graph (ORKG)  [ 7 ] , as such technical support by building, publishing, and analyzing a Knowledge Graph (KG) of empirical research in RE (KG-EmpiRE) based on currently 680 research track papers of the IEEE International Requirements Engineering Conference (1994-2022).

In this paper, we present the KG-EmpiRE, available in the ORKG 1 1 1 https://orkg.org/observatory/Empirical_Software_Engineering , and its analysis, available on GitHub  [ 8 ] , Zenodo  [ 9 ] , and on Binder 2 2 2 https://tinyurl.com/empire-analysis for interactive replication and (re-)use.

KG-EmpiRE contains scientific data on the six themes  research paradigm , research design , research method , data collection , data analysis , and bibliographic metadata . We plan to expand these themes in the long term. For more details on the themes, refer to the supplementary materials  [ 9 , 8 ] . Since its first release based on 199 papers (2014-2022)  [ 5 ] , KG-EmpiRE and its analysis have been updated twice. Karras et al.   [ 1 ] published the first update with 570 papers (2000-2022) at the 17th ACM/IEEE International Symposium on Empirical Software Engineering and Measurement 2023, where they received the best paper award. The second update is ongoing and covers 680 papers (1994-2022) so far. The goal for the second update is to cover all 748 research track papers from the IEEE International Requirements Engineering Conference  (1993-2023) .

The analysis provides answers to 16 out of 77 competency questions (cf. supplementary materials  [ 9 , 8 ] ) regarding empirical research in RE that we derived from the vision of Sjøberg et al.  [ 10 ] on the role of empirical methods in SE, including RE, for 2020-2025. While the number of competency questions answered reflects the coverage of the curated topic in KG-EmpiRE, the answers to competency questions provide insights into the state and evolution of empirical research in RE. For each competency question answered, we provide all details of the analysis with its data, visualizations, explanations, and answers in a repository  [ 9 , 8 ] that is also hosted on Binder for interactive replication and (re-)use.

Overall, this repository contains all files with detailed explanations and instructions for replication and (re-)use of KG-EmpiRE and its analysis locally or via executable environments (Binder and GitHub Codespaces), as well as for repeating the research approach for sustainable literature reviews with the ORKG. The repository also contains all generated visualizations with their data, exported as PNG and CSV files, as well as supplementary materials on the themes, their structuring in the ORKG, and all 77 competency questions.

II Structure of KG-EmpiRE and the Repository

Ii-a kg-empire.

We developed an ORKG template 3 3 3 https://orkg.org/template/R186491 to organize the scientific data extracted from the papers in the ORKG. ORKG templates implement a subset of the Shapes Constraint Language (SHACL) and allow specifying the underlying (graph) structure to organize the data in a structured manner  [ 11 ] . In this way, we determined which data to extract and standardized their description to ensure they are FAIR, consistent, and comparable across all papers. The developed ORKG template covers the six themes investigated. For more details on the ORKG template, refer to the supplementary materials  [ 9 , 8 ] .

By applying the ORKG template to the papers, KG-EmpiRE currently consists of almost 35,000 triples, which are made up of over 51,000 resources and almost 19,000 literals (see Table I ). While these statistics reflect the efforts to provide a solid structured description of the extracted data, they also show that KG-EmpiRE is relatively small compared to the entire ORKG and other well-known knowledge graphs, e.g., Wikidata or DBpedia, which include millions of entities.

II-B Repository

In the repository, there are three folders and six files, with the Jupyter Notebook empire-analysis.ipynb as the main file. The Jupyter Notebook encapsulates the entire analysis of KG-EmpiRE and provides visualizations, explanations, and answers for each of the 16 competency questions. The visualizations are exported as PNG files per competency question to the Figures folder. The data retrieved by KG-EmpiRE for analysis is stored as CSV files for each competency question in the SPARQL-Data folder by date. In this folder, we also provide CSV files of the latest release to replicate the results of the related publication  [ 1 ] . The last folder Supplementary materials provides additional materials for detailed overviews of the content for data extraction regarding the themes, the developed ORKG template, all 77 competency questions derived, and the research approach. The second most important file is README.md , which contains detailed explanations and instructions about the project, the repository, its installation (locally and via executable environments), the replication of the analysis, and the (re)use of KG-EmpiRE with its most recent data. The remaining four files support the installation ( requirements.txt , runtime.txt ), clarify the copyright ( LICENSE ), and ensure the citability of the repository ( CITATION.cff ) 4 4 4 https://citation-file-format.github.io/ .

III Conclusion

Overall, KG-EmpiRE and its analysis lay the foundation for a sustainable literature review on the state and evolution of empirical research in requirements engineering. They can be used to replicate the results from the related publication  [ 1 ] , (re-)use the data for further studies, and repeat the research approach for sustainable literature reviews on other topics. KG-EmpiRE and its analysis demonstrate how innovative infrastructures, such as the ORKG, can be leveraged to make data from literature reviews FAIR and openly available in the long term. In this way, researchers can build on and update the data ideally collaboratively, enabling sustainable literature reviews for comprehensive, up-to-date, and long-term available overviews, true to the principle: Divide et Impera .

In summary, the special feature of KG-EmpiRE lies in the proof that data from literature reviews can already be prepared during data extraction in such a way that they are understandable and processable by humans and machines to update, replicate, and (re-)use them sustainably. KG-EmpiRE and the underlying research approach using technical infrastructures, such as the ORKG, have the potential to be used on a large scale to establish sustainable literature reviews and thus ensure the quality, reliability, and timeliness of their research results.

Acknowledgment

The authors thank the Federal Government, the Heads of Government of the Länder, as well as the Joint Science Conference (GWK), for their funding and support within the NFDI4Ing and NFDI4DataScience consortia. This work was funded by the German Research Foundation (DFG) project numbers 44214671 and 460234259 and by the European Research Council for the project ScienceGRAPH (Grant agreement ID: 819536).

• [1] O. Karras, F. Wernlein, J. Klünder, and S. Auer, “Divide and Conquer the EmpiRE: A Community-Maintainable Knowledge Graph of Empirical Research in Requirements Engineering,” in International Symposium on Empirical Software Engineering and Measurement , 2023.
• [2] O. Karras, F. Wernlein, J. Klünder, and S. Auer, “A Comparison of Scientific Publications on the State of Empirical Research in Requirements Engineering and Software Engineering,” 2023. [Online]. Available: https://orkg.org/comparison/R650023/
• [3] V. dos Santos et al. , “Towards Sustainability of Systematic Literature Reviews,” in 15th ACM/IEEE International Symposium on Empirical Software Engineering and Measurement , 2021.
• [4] D. Mendez et al. , “Open Science in Software Engineering,” in Contemporary Empirical Methods in Software Engineering .   Springer, 2020.
• [5] F. Wernlein, “Acquisition and Analysis of Research Practices Using Semantic Structures,” Bachelor Thesis, Gottfried Wilhelm Leibniz Universität, 2022. [Online]. Available: https://doi.org/10.15488/12796
• [6] O. Karras, F. Wernlein, J. Klünder, and S. Auer, “KG-EmpiRE: A Community-Maintainable Knowledge Graph of Empirical Research in Requirements Engineering,” in Software Engineering 2024 , 2024.
• [7] M. Stocker et al. , “FAIR Scientific Information with the Open Research Knowledge Graph,” FAIR Connect , vol. 1, no. 1, 2023.
• [8] O. Karras, “Divide and Conquer the EmpiRE: A Community-Maintainable Knowledge Graph of Empirical Research in Requirements Engineering - A Sustainable Literature Review for Analyzing the State and Evolution of Empirical Research in Requirements Engineering,” 2023. [Online]. Available: https://github.com/okarras/EmpiRE-Analysis
• [9] ——, “Divide and Conquer the EmpiRE: A Community-Maintainable Knowledge Graph of Empirical Research in Requirements Engineering - A Sustainable Literature Review for Analyzing the State and Evolution of Empirical Research in Requirements Engineering,” 2024. [Online]. Available: https://doi.org/10.5281/zenodo.11092471
• [10] D. I. Sjoberg et al. , “The Future of Empirical Methods in Software Engineering Research,” in Future of Software Engineering , 2007.
• [11] H. Hussein et al. , “Increasing Reproducibility in Science by Interlinking Semantic Artifact Descriptions in a Knowledge Graph,” in Leveraging Generative Intelligence in Digital Libraries: Towards Human-Machine Collaboration .   Springer, 2023.

SYSTEMATIC REVIEW article

A systematic review of empirical studies incorporating english movies as pedagogic aids in english language classroom provisionally accepted.

• 1 Department of English, School of Social Sciences and Languages, Vellore Institute of Technology, Vellore, India., India
• 2 Research Scholar, Department of English, School of Social Sciences and Languages, Vellore Institute of Technology, Vellore, Tamil Nadu, India., India
• 3 Department of English, School of Social Sciences and Languages, VIT University, India

The final, formatted version of the article will be published soon.

The use of movie as an audio-visual multimodal tool has been extensively researched, and the studies prove that they play a vital role in enhancing communicative competence. Incorporating authentic materials like movies, television series, podcasts, social media, etc. into language learning serves as a valuable resource for the learners, for it exposes them to both official and vernacular language. The current study aims to systematically analyse the preceding studies that conjoined English movies into the curriculum to teach English. It also examines and evaluates the empirical research that various researchers conducted from 2000 to 2023. The articles were primarily sourced from prominent academic databases such as ProQuest, ScienceDirect, Scopus, Web of Science, and Google Scholar. Inclusion and exclusion criteria were applied in screening the 921 sources, of which 23 empirical studies were eligible for the review as a result of a three-stage data extraction process as shown in the "Preferred Reporting Items for Systematic Reviews and Meta Analyses" (PRISMA) chart. The extraction of data from the review encompasses an overview of the empirical studies, methodologies, participants, and interventions. The extracts were systematically analysed using the software's End Note and Covidence. The analysis of the existing literature and experimental data substantiates that teaching and learning English as a second or foreign language using movies as teaching aids exhibit promising prospects for enhancing English language proficiency. The findings of the study reveal different genres of movies that aid the facilitator in producing effective instruction materials with clearly defined objectives and guided activities. It is also observed that the learners have a positive experience with long-term learning benefits.

Keywords: EndNote, Covidence, Audio-visual multimodal aids, Communicative competence, Systematic review, Teaching Supplements

Received: 13 Feb 2024; Accepted: 16 May 2024.

Copyright: © 2024 K and S.N.S. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

* Correspondence: Dr. Gandhimathi S.N.S, Department of English, School of Social Sciences and Languages, VIT University, Vellore, India

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• Open access
• Published: 09 May 2024

Diabetes, life course and childhood socioeconomic conditions: an empirical assessment for Mexico

• Marina Gonzalez-Samano 1 &
• Hector J. Villarreal 1  

BMC Public Health volume  24 , Article number:  1274 ( 2024 ) Cite this article

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Demographic and epidemiological dynamics characterized by lower fertility rates and longer life expectancy, as well as higher prevalence of non-communicable diseases such as diabetes, represent important challenges for policy makers around the World. We investigate the risk factors that influence the diagnosis of diabetes in the Mexican population aged 50 years and over, including childhood poverty.

This work employs a probabilistic regression model with information from the Mexican Health and Aging Study (MHAS) of 2012 and 2018. Our results are consistent with the existing literature and should raise strong concerns. The findings suggest that risk factors that favor the diagnosis of diabetes in adulthood are: age, family antecedents of diabetes, obesity, and socioeconomic conditions during both adulthood and childhood.

Conclusions

Poverty conditions before the age 10, with inter-temporal poverty implications, are associated with a higher probability of being diagnosed with diabetes when older and pose extraordinary policy challenges.

Peer Review reports

One of the major public health concerns worldwide is the negative consequences that the demographic (with its epidemiological) transition could bring. This demographic transition is driven by increasing levels of life expectancy (caused by technological innovation and scientific breakthroughs in many cases) and decreasing fertility rates. While during the 20th century, the main health concerns were related to infectious and parasitic diseases, at the present time, non-communicable diseases (NCDs), such as diabetes, constitute a harsh burden in terms of economic and social impact. NCDs most commonly affect the health of adults and the elderly. The economic and social costs associated with NCDs increase sharply with age. These patterns have implications for economic growth, poverty-reduction efforts and social welfare [ 1 ].

Mexico’s demographic trends are reflecting a significant shift over the past decades, much like those observed globally. In 1950, the fertility rate stood at 6.7 children per woman, and the proportion of the population aged 60 or over was about 2%. Since the 1970s, there has been a considerable decrease in fertility rates; by 2017, it had dropped to 2.2 children per woman [ 2 ]. Even more pressing, according to CONAPO Mexico had a total fertility rate of 1.91 during 2023 [ 3 ]. Alongside the declining fertility, the aging population is becoming a more prominent feature in Mexico’s demographic profile. In 2017, individuals aged 60 and over constituted around 10% of the population. Forecasts for 2050 project that this figure will more than double, with those 60 and over representing 25% of the total population. These trends suggest substantial changes in Mexico’s population structure, with implications for policy-making in areas such as healthcare, pensions, and workforce development [ 2 ].

Regarding NCDs, in 2017 13% of the Mexican adult population suffered from diabetes, which is twice the Organisation for Economic Cooperation and Development (OECD) average and it is also the highest rate among its members. Some of the risk factors associated with this disease are being overweight or obese, unhealthy diets and sedentary lifestyles. In 2017 72.5% of the Mexican population was overweight or obese [ 4 ] and the country had the highest OECD rate of hospital admissions for diabetes. During the period of 2012 to 2017, the number of hospital admissions for amputations related to this condition, increased by more than 10%, which suggests a deterioration in quality and control of diabetes treatments [ 4 ]. Moreover, it is estimated that diabetes prevalence will continue with its upward trend; forecasts anticipate that in 2030 there will be around 17.2 million people in Mexico with this condition [ 5 ].

Despite the increasing proportion of older people, most of the research regarding the effects of socioeconomic conditions on health focuses on economically active populations. Those which do consider older people, do not investigate length factors such as childhood conditions [ 6 , 7 ]. In this sense, the Social Determinants of Health (SDH) throughout the Life Course approach provide a framework to ponder and direct the design of public policies on population aging and health [ 8 , 9 ]. They focus on well-being and the quality of life of populations from a multi-factorial perspective [ 10 , 11 , 12 ].

In this study, we explore the impact of childhood and adulthood conditions and other demographic and health aspects on diabetes among older people. The literature has proposed several mechanisms through which the mentioned drivers could operate. In general, these approaches imply that satisfactory socioeconomic outcomes for adults may relatively atone for poor socioeconomic conditions in early childhood [ 13 , 14 , 15 , 16 ].

Poverty conditions during the first years of life have critical implications, and yet children are twice as likely to live in poverty as adults [ 16 , 17 ]. On the other hand, poverty is known to be closely linked to NCDs such as diabetes. According to [ 13 ], NCDs are expected to obstruct poverty reduction efforts in low and middle-income countries (LMICs) by increasing costs associated with health care. Moreover, the costs resulting from NCDs such as diabetes could deplete household incomes rapidly and impulse millions of people into poverty [ 16 ].

The United Nations Children’s Fund (UNICEF) has highlighted the consequences of what it describes as the “invisible epidemic”: non-communicable diseases. NCDs are the leading cause of death worldwide, accounting for 71% or 41 million of the annual deaths globally. The majority (85%) of NCD deaths among people under 70 years of age occur in low and middle-income countries [ 17 ].

According the World Health Organization (WHO), SDH are non-medical factors that influence health outcomes, such as the circumstances in which people are born, grow, work, live, and age, and the broader set of forces and systems that shape the conditions of daily life Footnote 1 .

These forces include economic policies and systems, development agendas, social norms and policies, and political systems [ 11 , 18 ]. In this regard, SDH have an important influence on health inequities in countries of all income levels. Health and disease follow a social gradient, that is, the lower the socioeconomic status, a lesser health is expected [ 11 , 18 ].

On the other hand, the Life Course perspective distinguishes the opportunity to inhibit and control illnesses at key phases of life from preconception to pregnancy, infancy, childhood, adolescence, and through adulthood. This does not follow the health model where an individual is healthy until disease occurs, the trajectory is determined earlier in life. Evidence suggests that age related mortality and morbidity can be anticipated in early life with factors such as maternal diet [ 19 ] and body composition, low childhood intelligence, and negative childhood experiences acting as antecedents of late-life diseases [ 13 ].

The consequential diversity in the capacities and health needs of older people is not accidental. They are rooted in events throughout the life course and SDH that can often be modified, hence opening intervention opportunities. This framework is central in the proposed “Healthy Aging”. According to WHO [ 20 ], Healthy Aging is “the process of developing and maintaining the functional ability that enables well-being in older age”.

In this way, the Life Course and SDH approaches allow to better distinguish how social differences in health are perpetuated and propagated, and how they can be diminished or assuaged through generations. Several research efforts suggest that age related mortality and morbidity can be predicted in early life with aspects such as maternal nutrition, low childhood intelligence, difficult childhood experiences acting as antecedents of late-life diseases [ 13 ]. The Life Course acknowledges the contribution of earlier life conditions on adult health outcomes [ 15 , 21 ]. In addition, SDH have an important influence on inequality and, therefore, on people’s well-being and quality of life [ 22 ]. Trends in health literacy across life are also influenced by various SDH such as income, educational level, gender and ethnicity [ 23 ].

Finally, though the research that links early life conditions and health outcomes in adulthood is scarce in low and middle-income countries, our study aims to address the gaps in knowledge regarding the impact of childhood socioeconomic conditions on long-term health outcomes, including the prevalence of non-communicable diseases in LMICs. We specifically focus on the incidence of diabetes in Mexico. Advocating for early-life targeted interventions, we highlight the critical need to address the root causes of NCDs to reduce their impact on the most vulnerable groups. Utilizing data from the Mexican Health and Aging Study (MHAS), which provides comprehensive health, demographic, and socioeconomic information on individuals aged 50 and older, as well as details on their childhoods (before the age of 10) and family health backgrounds [ 24 ], our research emphasizes the importance of developing targeted interventions on early life course stages.

Health, childhood and adulthood conditions

Multiple studies highlight that childhood experiences can influence patterns of disease, aging, and mortality later in life [ 10 , 11 , 16 , 20 , 25 ]. The conditions in health and its social determinants accumulate over the life course. This process initiates with pregnancy and early childhood, continues throughout school years and the transition to working life and later in retirement. The main priority should be for countries to ensure a good start in life during childhood. This requires at least adequate social and health protection for women, plus affordable good early childhood education and care systems for infants [ 11 ].

However, demonstrating links between childhood health conditions and adult development and health is complex. Frequently, researchers do not have the data necessary to distinguish the health effects of changes in living standards or environmental conditions with respect to childhood illnesses [ 26 ]. A study conducted in Sweden, concluded that reduced early exposure to diseases is related to increases in life expectancy. Additionally, research with data from two surveys of Latin America countries found associations between early life conditions and disabilities later in life. In this sense, the study suggests that older people who were born and raised in times of poor nutrition and a higher risk of exposure to infectious diseases, were more likely to have some disability. In a survey in Puerto Rico, it was observed that the probability of being disabled was greater than 64% for people who grew up in poor conditions than for those who grew up in good conditions. Another survey that considered seven urban centers in Latin America found that the probability of disability was 43% higher for those with disadvantaged backgrounds, than for those with favorable ones [ 26 ].

Recent studies have focused on childhood circumstances to explain later life outcomes [ 12 , 27 , 28 , 29 , 30 , 31 ]. These research findings have shown the importance of considering socioeconomic aspects during childhood, including child poverty from a multidimensional perspective [ 12 ], as a determinant of health status of adults and health disparities. When disadvantaged as children, irreversible effects on health show-up frequently. One clear example is the association of socioeconomic aspects during childhood with type 2 diabetes and obesity in adulthood [ 32 , 33 ].

The future development of children is linked to present socioeconomic levels and social mobility in adulthood [ 27 ]. Some studies [ 28 , 34 , 35 ] indicate that the effects of childhood exposure to lower socioeconomic status or conditions of poverty on health in old age may persist independently of upward social mobility in adulthood. Hence, children who grow up in poverty are more likely to present health problems during adulthood, while those who did not grow up in poverty have a higher probability of remaining healthy.

Another important consideration regards developmental mismatches [ 36 ]. Their article emphasizes how developmental and evolutionary mismatches impact the risk of diseases like diabetes. There could be a disparity between the early life environment and the one encountered in adulthood, turning adaptations that were once beneficial into risk factors for non-communicable diseases. High-calorie diets and sedentary lifestyles could trigger diabetes prevalence.

If these connections between early life and health in old age can be established firmly, it is expected that aging people in low and middle-income countries have another disadvantage regarding elders in developed countries, including a higher risk of developing health problems in old age and frequently multiple NCDs [ 26 ]. Under this context, the effective management of NCDs such as diabetes is crucial, and childhood living standards would be a variable to ponder [ 26 , 37 ]. Work related to the Life Course approach has emphasized the importance of considering socioeconomic aspects during childhood, including poverty [ 12 ] as a determinant of adult health status and its disparities [ 28 , 29 , 30 , 31 ].

Data and methods

Data source.

The Mexican Health and Aging Study (MHAS) is a national longitudinal survey of adults aged 50 years and over in Mexico. The baseline survey has national, urban, and rural representation of adults born in 1951 or earlier. It was conducted in 2001 with follow-up interviews in 2003, 2012, 2015, 2018 and 2021 [ 38 ]. New samples of adults were added in 2012 and 2018 to refresh the panel. The survey includes information on health measures (self-reports of conditions and functional status), background (education and childhood living conditions), family demographics, and economic measures. The MHAS (Mexican Health and Aging Study) is partly sponsored by the National Institutes of Health/National Institute on Aging (grant number NIH R01AG018016) in the United States and the Instituto Nacional de Estadística y Geografía (INEGI) in Mexico. Data files and documentation are public use and available at www.MHASweb.org .

In this research, the analysis was based on data from the survey conducted in 2018 (it was the most recent when the project started, later the 2021 survey became available). The study focused exclusively on participants who were aged 50 or older at the time of the 2018 survey. To minimize response bias, the study included only observations from direct interviewees, excluding proxy respondents, and particularly those who completed the section of the questionnaire pertaining to “Childhood Characteristics before the age of 10 years” Footnote 2 . Furthermore, to expand the sample size, individuals who first joined the survey during the 2012 cycle were identified, utilizing data from both the 2012 and 2018 surveys [ 39 ]. After locating the same individuals in both datasets, responses related to childhood conditions from the 2012 survey were extracted and integrated into the 2018 dataset. Biases in the samples were not found. This approach resulted in a total sample size of 8,082 observations.

In addition, we selected a suite of predictor variables to provide a comprehensive examination of the demographic, socioeconomic, and health-related characteristics within our sample (Table 1 ). The cohort consists of 8,082 participants with males exhibiting a marginally higher mean age (58.3 years) compared to females (56.7 years). In terms of educational achievement, males attained a slightly higher level of schooling, averaging 8.3 years, as opposed to 7.6 years for females.

Regarding the spatial distribution of the study population reveals that 1,717 individuals reside in areas with 2,500 inhabitants or fewer, indicating a rural setting, while the majority, 6,365 individuals, are found in regions with more than 2,500 inhabitants, suggesting an urban setting. Among the subjects, a significant number of males (23%) are located in the former, rural settings, which is higher than their female counterparts (19.7%). The data on living arrangements indicate notable gender differences, with 86% of males cohabiting with partners against 68.8% of females. The state of being single-a term here encompassing a spectrum of prior marital experiences but currently not cohabiting-is observed in 31.2% of females and 14% of males. The socioeconomic dimension is gauged using “proxy variables” such as the absence of poverty in adulthood and presence of childhood poverty, both of which are evenly represented across genders. Health-related self-reporting data reveals that females have a higher incidence of diagnosed diabetes (24.4%) compared to males (20.1%), and a larger percentage of females (26.6%) manage their diabetes with insulin. The propensity for medication use to control diabetes is high among both sexes, though more pronounced in females (91.5%) relative to males (85.3%). Additionally, obesity rates, determined by a Body Mass Index Footnote 3 of 30 or greater, are substantially elevated in females (34.8%) versus males (24.6%). Furthermore, a familial history of diabetes is slightly more prevalent in females, affecting 32.6% with diabetic mothers and 20% with diabetic fathers.

There is a serious concern about self-reporting medical conditions, to what extent this information is reliable. For [ 40 , 41 ] the validity and high accuracy of self-reported diagnosis of diabetes mellitus has been confirmed by previous research, and previous studies using WHO data have also used this question to evaluate diabetes mellitus [ 42 , 43 ].

For the survey employed in this paper, [ 44 ] confirm a correspondence between self-reported and objective measures. Nonetheless, [ 45 ] warn about true prevalence and this kind of reporting. In addition, the implications of relying on diagnosed diabetes, rather than total diabetes prevalence, include the potential under-representation of the condition’s true prevalence due to undiagnosed cases. Since the study’s analysis is based on self-reported data from the Mexican Health and Aging Study, it might not capture those individuals who are unaware of their condition [ 45 ]. The existence of statistical biases could be a potential limitation in the analysis.

Equally or even more troublesome is the problem of recalling conditions during childhood. While some factors (depression among others) can produce limited recalling [ 46 ], specific conditions are well recalled, if not their details and timing [ 47 ].

Regarding the age distribution, the sample is mostly concentrated in three groups: 67.6% for individuals between 50 and 59 years of age, followed by 29.6% for those between 60 and 69 years of age, and 2.5% for those between 70 and 79 years of age. On average, the educational level for women is 7.6 years of schooling while for men it is 8.2 years, which suggests an incomplete level of secondary education for both. On the other hand, from the total number of women in the sample (4,368), 24% of them indicated the presence of diabetes, and 20% of men in the sample (3,714) reported this condition. In addition, around 68% of women with diabetes reported being overweight or obese, for men this percentage was 69%. Meanwhile, 71.4% women with diabetes reported parental history of diabetes, for men this percentage was 68%. The next subsections describe the construction and identification of the key dependent and independent variables.

Dependent variable

The dependent variable is binary, which refers to the individual’s diagnosis of diabetes. This variable was taken from section C of the basic questionnaire of the MHAS 2018. The question is as follows: Has a doctor or medical professional ever told you that you have diabetes? If the answer is “yes” it was assigned a value of 1 and if the answer was “no”, a 0. The absence of answers was left empty, non-imputed. Regarding the individuals who reported being diagnosed with diabetes, 94.2% were taking medication or using insulin injections or pumps, and / or following a special diet to manage diabetes, without statistical differences when interchanging the samples.

Independent variables

For the explanatory variables of the model, sociodemographic, socioeconomic (“proxy” Footnote 4 of poverty in childhood and non-poverty in old age) Footnote 5 , and geographical variables were considered, as well as other variables related the parents of the interviewees. Given the difficulty of constructing a robust variable that reflects respondents’ income, internet access was considered as a proxy variable that would allow to ascertain the poverty status of the individual in old age. Several tests were performed for robustness Footnote 6 .

Internet access in Mexico is more common among relative well-off Mexicans than it was among the poorest sector of the population. Thus, according to [ 49 , 50 ], 7 out of 10 individuals from the highest income segment were internet users, while for the lowest income deciles, this was only 2 out of 10. Furthermore, a low level of schooling was related to internet access opportunities. Therefore, people who only received primary education were 4 times less likely to use the internet in Mexico.

Additionally, for the variable of poverty during childhood, a proxy was considered which corresponds to the answer of the question “Before you were 10 years old, did your home have an indoor toilet?” Footnote 7 , United Nations Children’s Fund (UNICEF) collaborators [ 12 ], pointed out that the severe deprivation of sanitation facilities has critical long-term effects on various aspects of an infant. In this regard, UNICEF highlights the crucial importance of eradicating severe sanitation deprivation as a method to eradicate absolute child poverty, emphasizing that sanitation facilities should be a priority for children.

Statistical analysis

Linear Probability Models (LPM) define the probability:

They assume (require) that: i) \(Pr(Y=1 \mid X)\) is an increasing function in X for \(\beta _{0}>0\) , and ii) \(0 \le Pr(Y=1 \mid X) \le 1 \forall X\) .

This implies a cumulative distribution function that guarantees that for any value of the parameters of X , probabilities are well-defined, with values in the interval [0, 1].

The dependent variable to be explained is binary (diabetes diagnosis is 1 if the person has been diagnosed with diabetes and 0 for the person who has not been diagnosed with diabetes). Hence, a special class of regression models (with limited dependent variable), is considered. There are two probability models with these characteristics frequently used: the Logit model, and the Probit model. In relation to this, [ 48 ] points out that, theoretically, both models are very similar. A potential advantage of Probit models is they could feed other related inquiries. For example, when testing selection via Inverse Mill’s Ratios.

The Probit model is expressed as:

In the Probit model with multiple regressors, \(X_1,X_2,\ldots ,X_k\) , \(\phi (.)\) the cumulative standard normal distribution function is \(\phi (Z)=P(X\le z)\) , \(Z\sim N(0,1)\) .

Therefore, in ( 2 ) \(P(Y=1 \mid X_1,X_2,\ldots ,X_k )\) means the probability that an event occurs given the values of other explanatory variables, where Z is distributed as a standard normal \(Z\sim N(0,1)\) . While a series of tests could be performed in the model, two are critical for this investigation: the linearity between the independent variables and the underlying latent variable, and the normality of errors.

In ( 2 ), the coefficient \(\beta _{1}\) represents the change in z associated to a unit of change in \(X_1\) . It is then observed that, although the effect of z on a change is linear, the link between z and the dependent variable Y is not linear since \(\phi\) is a non-linear function of X . Therefore, the coefficients of X do not have a simple interpretation. In that sense, marginal effects must be calculated. Considering that in the linear regression model, the slope coefficient measures the change in the average value of the returned variable, due to a unit of change in the value of the regressor, maintaining the other variables constant. In these models, the slope coefficient directly measures the change in the probability of an event occurring, as a result of a unit change in the value of the regressor, holding all other variables constant, a discussion can be found at [ 51 ]. The \(\beta\) parameters are frequently estimated by maximum likelihood. The likelihood function is the joint probability distribution of the data treated as a function of the unknown coefficients Footnote 8 .

The maximum likelihood function is the conditional density of \(Y_1,\ldots ,Y_k\) given \(X_1,\ldots ,X_k\) as a function of the unknown parameters \(\beta\) . Thus, the Maximum Likelihood Estimation (MLE) is the value of the parameters \(\beta\) that maximizes the maximum likelihood function. Hence, the MLE is the value of \(\beta\) that best describes the distribution of the data. In this regard and in large samples, the MLE is consistent, normally distributed, and efficient (it has the lowest variance among all the estimators). The \(\beta\) is solved by numerical methods. The resulting \({\hat{\beta }}\) is consistent, normally distributed, and asymptotically efficient.

A Probit model is proposed as follows. The dependent variable is diagnosed diabetes in adulthood correlated to several independent variables: sex, age, marital status, locality size, a dummy variable (to identify observations sourced from the 2012 survey wave, which is focused on childhood-related questions), obesity condition (Body Mass Index \(\ge\) 30), family history of diabetes, childhood poverty, no poverty in adulthood and the interaction of childhood poverty and no poverty in adulthood.

The variables should have analogous probability distributions and behave mutually independent. If errors violate the assumptions, the estimated values would be biased and inconsistent. Therefore, estimated values will also be shown with the Linear Probability Model.

In this type of model, \(y_i\) is a latent dependent variable that takes values of 1 if the person has been diagnosed with diabetes, that is, if individual i has a certain characteristic or quality and 0 otherwise; X is a set of explanatory variables that are assumed to be strictly exogenous, which implies that \(Cov\left[ x_i,\varepsilon _j\right] =0\ \forall\) the i individuals. In addition, the error term \(\varepsilon\) is assumed to be i . i . d . In this way, the probability of an event occurring given a set of explanatory variables is obtained:

In ( 1 ) G is a function that strictly takes values between 0 and 1, \(0<G(z)<1\) , for all real numbers z . As noted at the beginning of this section, in the Probit model, G represents a standardized normal cumulative distribution function given by:

Finally, to know the effects of the changes in the explanatory variables on the probability of the event occurring, a partial derivative can show that:

The term \(g\left( z\right)\) corresponds to a probability density function. Since the Probit model \(G\left( .\right)\) is a strictly positive cumulative distribution function, \(g\left( z\right) >0\ \forall \ z\) , the sign of the partial effect is the same as that of \(\beta _j\) .

This section reviews the factors associated with the probability of being diagnosed with diabetes for men and women and discusses their significance. Table  2 summarizes the main results of the Probit model.

Sociodemographic

Marginal effects on the dependent variable show that the age of individuals is highly significant with a positive correlation. This suggests that age is a factor leading to a higher probability (1%) of obtaining a diagnosis of diabetes, which could imply that as the person ages, the likelihood of developing diabetes increases. This result is consistent with studies conducted on the age-related decline in mitochondrial function, which in turn contributes to insulin resistance in old age. These conditions may foster the development of glucose intolerance and type 2 diabetes [ 53 , 54 ].

In addition, the outcomes indicate that women have an associated probability increase of 4% of suffering from this disease compared to men Footnote 9 . Regarding the differences by marital status, women and men living in a couple have a higher probability of being diagnosed with diabetes. In a study for Mexico using MHAS 2012, [ 45 ] found that being a woman and being married are significantly associated with a higher likelihood of self-reported diabetes Footnote 10 .

On the other hand, the results by size of locality suggest that individuals residing in urban areas have a non-negligible higher probability of suffering from diabetes compared to people living in rural locations. This is in line with the phenomenon of “nutritional transition”, which initially occurred in high-income countries and later in low-income countries, first in urban areas and then in rural areas [ 56 , 57 ]. For Mexico, [ 58 ] despite the prevalence of diabetes presents heterogeneous patterns, this condition is strongly greater in urban areas compared with rural areas.

Health and lifestyle

The results suggest a significant positive effect on the probability of diagnosis of diabetes for the individuals in the sample when the father and/or mother have this condition. In the case of a mother with diabetes, the associated probability of diabetes is 13%, while for a father with diabetes, it is 12%. Additionally, obesity is an important risk factor in the diagnosis of diabetes, the linked marginal effect of this comorbidity in the diagnosis of diabetes is 4%. In this regard, no significant differences were found by sex or locality size Footnote 11 .

Socioeconomic

The findings indicate a lower probability that individuals are diagnosed with diabetes if during adulthood they are not poor (-5%). On the other hand, from the interaction of the variables poverty in childhood and non-poverty in old age, a considerable positive effect is observed. This suggests that when the individual was poor in childhood, despite no longer poor in adulthood, the probability associated with the diagnosis of diabetes is positive and significant. Thus, it is possible that conditions of poverty in childhood influence the development of this disease later in life Footnote 12 . While this is a correlation, the fact that an interaction of socioeconomic characteristics has bigger linear effect than a key biological characteristic (obesity) is non trivial, and reinforces the importance of life course analysis.

Social mobility, defined as the change in an individual’s socioeconomic status relative to their parents or over their lifetime, is a crucial metric for assessing equal opportunity-a measure of whether people have the same chances to achieve success regardless of their initial socioeconomic position. Our study aligns with the broader evidence [ 65 , 66 ], suggesting that those from disadvantaged backgrounds often face significant barriers to socioeconomic advancement Footnote 13 .

A compelling finding of this paper, refers how poverty conditions during childhood remain an important risk factor associated with the greater probability of being diagnosed with diabetes during adulthood in Mexico. Despite these circumstances do not determine the diagnosis of diabetes in older adults, they have a strong correlation with the ailment. On the other hand, even when individuals have not experienced poverty during childhood, but it occurs during adulthood, the probability associated with the diagnosis of diabetes increases. Not surprisingly, the probability of being diagnosed with diabetes scales when the person was poor in both stages. These effects are persistent for men and women, although for women the associated probability was higher than for men. Likewise, there is a positive and high correlation of the parents’ history of diabetes and the obesity condition on the probability of developing this disease. Biological aspects could be present, but also modifiable factors, with the generational transmission of elements related to lifestyle (eating habits and physical activity). Similarly, people who live with a partner have a higher associated probability of being diagnosed with diabetes. The literature suggests that this is due to the tendency of individuals to select spouses based on the preference for similar phenotype characteristics and the convergence of their behaviors and lifestyle. Moreover, these issues have been exacerbated by urbanization processes and by the “food transition” Footnote 14 that has made processed and ultra-processed products more and more accessible. Such products are characterized by being high in fat, salt, and sugar. Regarding the effect of the size of the locality on the probability of being diagnosed with diabetes, the results show differences for people residing in rural and urban areas. In urban localities, the associated probability is higher compared to rural ones. Likewise, aging is an important factor that affects the probability of suffering from diabetes: as the individual ages, the probability of developing this disease increases.

In terms of the analysis and empirical strategy used, the findings show valuable relationships. Aligned with efforts to improve the accuracy and reliability of health data by combining biomarkers and objective measurements with self-reported data [ 70 ], biomarkers in the survey were employed. These biomarkers were used for diabetes (the dependent variable) and obesity condition (as one of the independent variables) in the model of Results  section. The results are consistent with the previous findings (See Appendix ).

There is ample space for additional work and get over the limitations of this work. For example, being MHAS a longitudinal survey, an econometric model can be developed in order to explore (test) causal relationships among the extensive set of variables. Also self-reporting could present different types of biases. While the use of biomarkers was an important robustness test, calculating bounds and checking selection biases would be valuable. Moreover, the survey also captures information related to social protection variables and social programs transfers, which could be useful for testing policies.

Given the interconnection of childhood conditions and the importance of these in the development of adult capacities and their success in their future life, they should be considered within the design and formulation of public policies and programs. The policies should focus and prioritize objectives of reducing the inequality gaps and pre-existing poverty in the country. Adopting measures to reduce inequalities in the social sphere is essential to protect future generations. In this sense, it is important to act on the Social Determinants of Health throughout the course of life in a broader social and economic context. Acting on the SDH would improve prospects for health and generate considerable social benefits that would allow people to achieve their capabilities and reduce the intergenerational perpetuation of inequalities. Thus, the SDH together with the Life Course approach, provide a sensible framework to identify risk clusters that can be broken in periods of effective interventions (e.g. childhood), as well as to improve the design of public policies on population aging and health, from a perspective focused on the well-being and quality of life of the Mexican population.

In this way, and to face the demographic transition and the diabetes epidemic in Mexico, comprehensive public policies that consider interventions from childhood will be required to reduce inequality and poverty. For some years now, the WHO has emphasized the importance and role of the inclusion of long-term care policies and programs focused on older adults. The forecasts in case of untimely acting indicate a significant negative effect on the social, economic and health structures for the coming years.

Finally, despite the increase of older population, much of the research on the effects of socioeconomic conditions on health is concentrated in economically active populations, and those ignore older people, and pay restricted attention to long term factors such as childhood conditions. The results presented in this document contribute to studies on population aging and public health. Evidence is found with respect to health determinants in a demographic group that is growing rapidly and not sufficiently considered.

Availability of data and materials

Data files and documentation are public use and available at www.MHASweb.org . Data and code used during the current study are available from the corresponding author on reasonable request.

Social Determinants of Health. Retrieved from https://www.who.int/health-topics/social-determinants-of-health#tab=tab_1 . Accessed on January 22, 2024.

Given the survey design, people responding the childhood questionnaire are new participants.

A Body Mass Index (BMI) was constructed considering the variables of height and weight reported in the MHAS 2018 survey (C6: “What is your current weight in kilograms?”, C67: “What is your height without shoes in meters?”). For adults, the World Health Organization (WHO) defines overweight as a BMI of 25 or higher, and obesity as a BMI of 30 or higher. BMI was calculated by dividing a person’s weight in kilograms by the square of their height in meters (kilograms/m 2 ).This information is available at: https://www.who.int/es/news-room/fact-sheets/detail/obesity-and-overweight , accessed on January 10, 2024.

In this context, the term “proxy”, was employed to describe variables that serve as stand-ins for factors that are not directly observable within our dataset. As noted by [ 48 ].

Numerous variables that could reflect household income were tested, but since they were self-reported and not part of the survey’s core, there is a large number of missing values.

We thank one referee for her suggestions regarding education years.

This question is found in section J.18 of the basic questionnaire and corresponds to the question “Does this home have ... internet?” If the person answers “yes”, that means that they have internet service and were assigned a value of 1, and 0 if the person does not have this service.

There is an interesting possibility of comparing the linear marginal effects with direct estimations from a Logit model (risk differences), [ 52 ]. We thank a referee for pointing this out.

This is consistent with what was stated in Aging in Mexico: The Most Vulnerable Adults of the MHAS Newsletter: May 20-2, 2020, which indicates that women are more likely to report diabetes than men. Retrieved from http://www.enasem.org/images/ENASEM-20-2-Aging_In_Mexico_AdutosMasVulnerables_2020.pdf . Accessed on February 10, 2024.

Furthermore, Danish researchers found a connection between the Body Mass Index of one spouse and the other spouse’s risk of developing type 2 diabetes. According to this study, spouses tend to be similar in terms of body weight, as people often tend to marry someone similar to themselves and share dietary and exercise habits when living together [ 55 ].

It has long been known that type 2 diabetes is, in part, hereditary. Family studies have revealed that first-degree relatives of people with type 2 diabetes are approximately 3 times more likely to develop the disease than people without a positive family history of the disease [ 59 , 60 , 61 ]. Likewise, in a study for Mexico, [ 62 ] point out that obesity and a history of type 2 diabetes in parents and genes play an important role in the development of type 2 diabetes. Furthermore, [ 63 ], points out that the frequency of diabetes mellitus also varies between different races and ethnicities.

This is consistent with the research by [ 64 ] who find that the conditions in which the person lived at the age of 10 affect health in old age.

According to [ 67 ] in a regional analysis on the degree of social mobility in Mexico, it indicates that social mobility is higher than the national average in the North and Central North regions, similar to the national average in the Central region, and lower than the average in the South region. In particular, it notes that children of poor parents made above-average progress if they grew up in the northern region, and less than average progress if they grew up in the southern region.

The country’s food environment has been transformed; it is becoming easier to access unhealthy products. In this sense, for the last 40 years, important changes have been observed in the Mexican diet, mainly from fresh and unprocessed foods to processed and ultra-processed products with a high content of sugar, salt, and fat. Marrón-Ponce et al. [ 68 ], point out that in 2016 around 23.1% of the energy in the Mexican population’s diet came from ultra-processed products, even though the WHO recommendations suggest that at most, this percentage should present between 5 and 10% of total energy per day. In addition, Mexico is the worldwide largest consumer of sugary beverages; its consumption represents approximately 10% of the total daily energy intake in adults and children and constitutes 70% of the total added sugar in the diet [ 69 ].

The study incorporates biomarkers to evaluate health conditions related to diabetes and obesity. Glycosylated hemoglobin results are employed as an indicator of diabetes [ 71 ], with a value equal to or exceeding 6.5% signifying a positive diagnosis (coded as “1”), while values below this threshold are coded as “0”, indicating the absence of the condition. Concurrently, Body Mass Index (BMI) is calculated from weight and height measurements to determine obesity, with a BMI of 30 or more classified as obese. These biomarkers provide quantifiable and reliable means of assessing the presence of these two critical health issues within the study’s population.

Abbreviations

Mexican Health and Aging Study

non-communicable diseases

Social Determinants of Health

World Health Organization

Organisation for Economic Cooperation and Development

National Institute of Statistics and Geography

United Nations Children’s Fund

Low and middle-income countries

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MGS (Marina Gonzalez-Samano) contributed to the design of the study and the final document with guidance and conceptual insights from HJV (Hector J. Villarreal). MGS and HJV carried out the search, analysed the documents and wrote the first draft of the article. All authors were involved in the conception of the research, revisions and editing of the article. All authors read and approved the final manuscript.

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For robustness testing a model specification was employed where self-reported diabetes and obesity measures are substituted with biomarkers obtained from the MHAS 2012. Table 3 summarizes the main results of the Probit model.

The analytical results from Table  2 (Model 1), and those derived from the utilization of biomarkers in Table 3 (Model 2) exhibit a considerable likeness, especially in the context of diabetes and obesity indicators. Notably, there is a significant reduction in the sample size when biomarkers Footnote 15 are introduced, which might account for the increased standard errors observed in Table 3. Consequently, certain variables such as: being “woman”, “living with a couple” and “residing in an urban locality”, have lost statistical significance in the biomarker analysis. Despite these differences, the general conclusions derived from this specification remain consistent with those presented in Model 1 (Table  2 ). Moreover, the linear effect of the interaction effect of poverty in childhood with no poverty in adulthood is bigger with the biomarker specification. Nonetheless, the larger confidence intervals need to be considered.

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Gonzalez-Samano, M., Villarreal, H. Diabetes, life course and childhood socioeconomic conditions: an empirical assessment for Mexico . BMC Public Health 24 , 1274 (2024). https://doi.org/10.1186/s12889-024-18767-5

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