law enforcement intelligence research paper

Artificial Intelligence and Law Enforcement

First Online: 30 November 2019

Cite this chapter

law enforcement intelligence research paper

Timo Rademacher 3

6521 Accesses

15 Citations

11 Altmetric

Artificial intelligence is increasingly able to autonomously detect suspicious activities (‘smart’ law enforcement). In certain domains, technology already fulfills the task of detecting suspicious activities better than human police officers ever could. In such areas, i.e. if and where smart law enforcement technologies actually work well enough, legislators and law enforcement agencies should consider their use. Unfortunately, the German Constitutional Court, the European Court of Justice, and the US Supreme Court are all struggling to develop convincing and clear-cut guidelines to direct these legislative and administrative considerations. This article attempts to offer such guidance: First, lawmakers need to implement regulatory provisions in order to maintain human accountability if AI-based law enforcement technologies are to be used. Secondly, AI law enforcement should be used, if and where possible, to overcome discriminatory traits in human policing that have plagued some jurisdictions for decades. Finally, given that smart law enforcement promises an ever more effective and even ubiquitous enforcement of the law—a ‘perfect’ rule of law, in that sense—it invites us as democratic societies to decide if, where, and when we might wish to preserve the freedom to disobey the rule(s) of law.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Available as PDF
Read on any device
Instant download
Own it forever
Available as EPUB and PDF
Compact, lightweight edition
Dispatched in 3 to 5 business days
Free shipping worldwide - see info
Durable hardcover edition

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

In doing so, this Chapter also complements the more specific analyses provided by Buchholtz, Schemmel, paras 32–46, and Braun Binder, paras 16 et seq., on legal tech, financial market regulation, and tax law enforcement, respectively.

For the general definition of artificial intelligence informing this Book cf. Rademacher and Wischmeyer, paras 5–6.

Or some other incident requiring state interference, e.g. an attempted suicide or the identification of a person for whom an arrest warrant is issued.

That excludes more ‘traditional’ big data technologies such as breathalyzers, field testing kits, or DNA analyses. This definition is similar to the one proposed by Rich ( 2016 ), pp. 891–892, who correctly notes that even though ‘[t]hese traditional technologies can be exceptionally helpful to police in establishing the “historical facts” of what happened’, they cannot analyze on their own ‘groups of disparate facts together and [draw] conclusions about the probability of an individual’ non-compliance. It is the specific feature of smart law enforcement technologies—called ‘automated suspicion algorithms’ by Rich—that they attempt to apply patterns that are woven with such density that a ‘match’ qualifies, per se, as indicative of suspicious activity (cf. para 29). Furthermore, the definition also excludes so-called ‘impossibility structures’, i.e. technology which not only detects suspicious activity, but at a more sophisticated level aims at making illegal conduct physically impossible (cf. Rich 2013 , pp. 802–804; see also, with a different terminology, Cheng 2006 , p. 664: ‘Type II structural controls’, Mulligan 2008 , p. 3: ‘perfect prevention’, and Rosenthal 2011 , p. 579: ‘digital preemption’).

Cf., for a definition, in this Book Rademacher and Wischmeyer, and the thorough explanation offered by Rich ( 2016 ), pp. 880–886, esp. 883.

See, e.g., Big Brother Watch ( 2018 ), pp. 25–33; Chaos Computer Club ( 2018 ).

Compare to the technical limitations still described by Candamo et al. ( 2010 ), esp. p. 215. For video summarization technologies cf. Thomas et al. ( 2017 ).

Ferguson ( 2017 ), pp. 88–90.

Big Brother Watch ( 2018 ), pp. 25–30; on the current legal framework governing the use of CCTV and comparable surveillance technologies in the UK cf. McKay ( 2015 ), paras 5.181 et seq.

The historical sensitivity, of course, translates into a rather restrictive interpretation of constitutional rules when it comes to video surveillance, cf. Wysk ( 2018 ), passim; Bier and Spiecker gen Döhmann ( 2012 ), pp. 616 et seq., and infra , paras 17–18.

Bundesministerium des Innern ( 2018 ). But cf. Chaos Computer Club ( 2018 ), rebutting the Ministry’s optimistic evaluation. Similar RWI ( 2018 ).

Bundespolizeipräsidium ( 2018 ), p. 35.

The EU-funded iBorderCtrl-project ( www.iborderctrl.eu ) is testing software that is to detect persons lying at border controls; see, for a first assessment, Algorithm Watch ( 2019 ), pp. 36–37.

E.g. Bouachir et al. ( 2018 ): video surveillance for real-time detection of suicide attempts.

Davenport ( 2016 ); Joh ( 2014 ), pp. 48–50; for further examples see Capers ( 2017 ), pp. 1271–1273. Comparable systems are being tested in Germany, too, cf. Djeffal, para 9, and Wendt ( 2018 ).

Ferguson ( 2017 ), p. 86.

In the final version of what is now Directive (EU) 2019/790 the explicit reference to content recognition technologies has been removed (cf. Article 17 of the Directive). If that effectively avoids a de facto obligation on information service providers to apply such filter technologies remains to be seen. For an early discussion of technological means of ‘automatic enforcement’ see Reidenberg ( 1998 ), pp. 559–560. See also Krönke, para 44, who expects a ‘de facto obligation’ of service providers to apply recognition software.

Ferguson ( 2017 ), pp. 114–118. For the Israeli intelligence agencies’ reportedly extensive and successful use of social media monitoring in detecting terrorists cf. Associated Press ( 2018 ); on terrorism in general see also Pelzer ( 2018 ).

Including well-established techniques such as fingerprint and DNA analysis. Cf. for a rather critical overview of ‘new’ technologies Murphy ( 2007 ), pp. 726–744; on more modern projects see Ferguson ( 2017 ), pp. 116–118.

Ferguson ( 2017 ), p. 118.

Rich ( 2016 ), p. 872; for an up-to-date account on that technology see Schemmel, para 32 and Throckmorton ( 2015 ), pp. 86–87; on data mining aimed at predicting tax avoidance see Lismont et al. ( 2018 ) and Braun Binder and with an example from Australia see Djeffal, para 11.

E.g. the private Polaris Project, which analyzes telephone calls for help in cases of human trafficking, to reveal places, routes, and even financial cash flows worthy of police attention.

See Eubanks ( 2018 ), for a critical report on software predicting child abuse tested in Allegheny County; see Spice ( 2015 ), reporting on a DARPA funded software to detect sex trafficking by screening online advertisements.

To some extent that includes misconduct within police forces as well, cf. Ferguson ( 2017 ), pp. 143–162.

Cf. Ferguson ( 2017 ), pp. 63–69. Commercial applications used in the US include HunchLab, Risk Terrain Modelling (RTM, cf. Caplan and Kennedy 2016 , and Ferguson 2017 , pp. 67–68), and PredPol.

For an up-to-date overview of place-based predictive policing in Germany cf. Seidensticker et al. ( 2018 ) and, for a criminological evaluation, Singelnstein ( 2018 ), pp. 3–5; for a comprehensive approach, which includes other forms of big data policing, cf. Rademacher ( 2017 ), pp. 368–372.

For the constitutional constraints German police has to respect see para 18. A second reason for the German reluctance might also be that big data driven proactive ‘rasterizing’ proved spectacularly inefficient when German police applied it in the aftermath of 9/11, cf. German Constitutional Court 1 BvR 518/02 ‘Rasterfahndung’ (4 April 2006), BVerfGE 115, pp. 327–331.

Cf. Burkert ( 2012 ), p. 101: ‘aimed at erecting Chinese walls within the executive’. This conception replaced the former idea of informational ‘Einheit der Verwaltung’ (executive unity), which prevailed, approximately, until the 1970s, cf. Oldiges ( 1987 ), pp. 742–743.

Rather negatively evaluated by Saunders et al. ( 2016 ), but reported to have improved significantly since 2015, cf. Ferguson ( 2017 ), p. 40.

Brühl ( 2018 ). On the federal ‘Polizei 2020’ program aimed at establishing an integrated database for all German police agencies, cf. Bundesministerium des Innern ( 2016 ).

Brühl ( 2018 ). The legal basis for this data mining is presumably Section 25a(1), (2) Hessisches Gesetz über die öffentliche Sicherheit und Ordnung (HSOG), in force since 4 July 2018.

Section 10 Geldwäschegesetz; cf. for a case study on anti-money laundering technology Demetis ( 2018 ); see also Schemmel, para 12.

Directive (EU) 2016/681 of 27 April 2016 on the use of passenger name record (PNR) data for the prevention, detection, investigation and prosecution of terrorist offences and serious crime. For an evaluation against the backdrop of EU and German constitutional law cf. Rademacher ( 2017 ), pp. 410–415.

For a status quo report on German PNR-analyses cf. Bundestag ( 2018 ).

See, for a rare example, Ferguson ( 2017 ), p. 88.

Schlossberg ( 2015 ).

For an early instance of aural surveillance cf. Zetter ( 2012 ).

Saracco ( 2017 ); see also May ( 2018 ), on AI designed to detect doping athletes.

Cf. Rademacher ( 2017 ), pp. 373–393; Rich ( 2016 ), pp. 880–886, 895–901.

Joh ( 2019 ), p. 179.

Henderson ( 2016 ), p. 935: ‘[W]hen it comes to criminal investigation, time travel seems increasingly possible.’

If police tried ‘to see into the past’ (Ferguson 2017 , p. 98) before the rise of big data policing, they usually had to rely on human eye witnesses—who are notoriously unreliable.

See Barret ( 2016 ), reporting on technology that could connect up to 30 million CCTV cameras in the US.

Cf. German Constitutional Court 1 BvR 370/07 ‘Onlinedurchsuchung’ (27 February 2008), BVerfGE 120, pp. 344–346, concerning intelligence agents performing website searches.

Article 13 Grundgesetz (German Basic Law).

Fundamental right to confidentiality and integrity of IT systems, developed by the Constitutional Court in ‘Onlinedurchsuchung’ (see note 44), pp. 302–315. On that right cf. Heinemann ( 2015 ), pp. 147–171; Hoffmann-Riem ( 2008 ), pp. 1015–1021; Böckenförde ( 2008 ).

Constitutional Court ‘Onlinedurchsuchung’ (see note 44), p. 345.

E.g. German Constitutional Court 1 BvR 2074/05 ‘KFZ-Kennzeichenkontrollen’ (11 March 2008), BVerfGE 120, p. 431; cf. Rademacher ( 2017 ), pp. 403–405, 406–407.

See, e.g., German Constitutional Court ‘KFZ-Kennzeichenkontrollen’ (see note 48), p. 430 with p. 399: Although an ALPR-scan was said to not count as an interference with A’s right to informational self-determination, if A’s license plate number does not produce a match in the database and the image was therefore deleted immediately, A’s potential privacy fear (‘chilling effect’ due to a feeling of constant observation’) still should render the interference with B’s right to informational self-determination—whose license plate number had produced a ‘hit’—unconstitutional. Just after the Federal Administrative Court confirmed that irritating jurisprudence, the Constitutional Court reversed it, now holding that any form of video surveillance amounts to an interference with the right to informational self-determination, see Constitutional Court 1 BvR 142/15 ‘KFZ-Kennzeichenkontrollen 2’ (18 December 2018), paras 45. Cf. Marsch ( 2012 ), pp. 605–616.

German Constitutional Court 1 BvR 209/83 ‘Volkszählung’ (15 December 1983), BVerfGE 65, p. 42: ‘[…] right of individuals to decide in principle themselves when and within what limits personal matters are disclosed’. Author’s translation.

Cf. Constitutional Court ‘Volkszählung’ (see note 50), p. 45: ‘in that respect, “unimportant” data no longer exist in the context of automated data processing’. Author’s translation.

The Sphärentheorie offered, in principle, heightened protection from surveillance for information that could be categorized as intimate or private, and did not protect, again in principle, information that was considered social or public.

Cf. Ferguson ( 2014 ), pp. 1313–1316.

Cf. Poscher ( 2017 ), pp. 131–134; Marsch ( 2018 ), pp. 116–124.

Cf. Marsch, paras 15–16. Ditto Poscher ( 2017 ), p. 132.

Constitutional Court ‘KFZ-Kennzeichenkontrollen’ (see note 48), and ‘KFZ-Kennzeichenkontrollen 2’ (see note 49).

Constitutional Court ‘Rasterfahndung’ (see note 27).

Cf. Staben ( 2016 ), pp. 160, 162 for a broad overview, listing most, if not all, the criteria the Constitutional Court so far has weighed against modern surveillance technologies.

See, esp., the decision on dragnet investigations, Constitutional Court ‘Rasterfahndung’ (see note 27), pp. 354, 356–357.

Constitutional Court ‘KFZ-Kennzeichenkontrollen’ (see note 48), pp. 401, 407.

Constitutional Court ‘Volkszählung’ (see note 50), p. 43; Constitutional Court ‘KFZ-Kennzeichenkontrollen’ (see note 48), pp. 402, 430; Constitutional Court ‘KFZ-Kennzeichenkontrollen 2’ (see note 49), paras 51, 98.

The Constitutional Court’s recurrence to this line of argument (‘risks of abuse’, see e.g. Constitutional Court ‘KFZ-Kennzeichenkontrollen’ (see note 48), p. 402) has been harshly criticized by German legal scholarship for being empirically unfounded, cf. Trute ( 2009 ), pp. 100–101.

See also Hermstrüwer, paras 10, 19.

Whether that being an imminent risk of harm to an individual’s health, life, or liberty, or whether police experience regarding a specific place (crime hot spots) or activity suffices (see for examples of the latter Constitutional Court ‘KFZ-Kennzeichenkontrollen 2’ (see note 49), para 94, and Rademacher 2017 , pp. 401–410), depends on the ‘invasiveness’ of the respective means of surveillance. Obviously, that again requires a proportionality test, the outcome of which is hard to predict. In specific areas, such as tax law (see Braun Binder) and financial regulation (see Schemmel, paras 10–12 and para 32 for US law), state databases are ‘rasterized’ routinely, thus implementing a limited form of generalized suspicion (Generalverdacht) or so-called ‘anlasslose Kontrolle’ (cf. Constitutional Court ‘KFZ-Kennzeichenkontrollen 2’ (see note 49), para 94).

Constitutional Court ‘KFZ-Kennzeichenkontrollen’ (see note 48), p. 378, recital 4.

Rademacher ( 2017 ), pp. 401–403.

Marsch ( 2018 ), pp. 17–30. Cf. for an up-to-date account of the ECtHR’s jurisprudence on surveillance technology Bachmeier ( 2018 ), pp. 178–181.

Most recently confirmed in CJEU Case C-207/16 ‘Ministerio Fiscal’ (2 October 2018) para 51. The respective jurisprudence is mainly based on Article 8 CFR [Right to data protection]. For a detailed analysis of that provision and the shift in the CJEU’s jurisprudence towards an understanding similar to the German right to informational self-determination cf. Marsch, paras 29–32.

See, above all, Regulation (EU) 2016/679 of 27 April 2016 on the protection of natural persons with regard to the processing of personal data and on the free movement of such data (General Data Protection Regulation); Directive (EU) 2016/680 of 27 April 2016 on the protection of natural persons with regard to the processing of personal data by competent authorities for the purpose of the prevention, investigation, detection or prosecution of criminal offences or the execution of criminal penalties, and on the free movement of such data; and Directive (EG) 2002/58 of 12 July 2002 concerning the processing of personal data and the protection of privacy in the electronic communications sector (ePrivacy Directive). Cf. Dimitrova ( 2018 ).

Cf. CJEU Case C-203/15 ‘Tele2 Sverige’ (21 December 2016) para 111: ‘[N]ational legislation [requiring private companies to store communications data] must be based on objective evidence which makes it possible to identify a public whose data is likely to reveal a link, at least an indirect one, with serious criminal offences […].’ Confirmed by CJEU Opinion No. 1/15 ‘Passenger Name Record’ (26 July 2017), para 191. But cf. the referrals submitted under Article 267 TFEU by the Investigatory Powers Tribunal London (C-623/17), the French Conseil d’État (C-512/18), and the Belgian Constitutional Court (C-520/18), rather critically scrutinizing the CJEU’s data protection-friendly approach. See also ECtHR Case-No. 35252/08 ‘Big Brother Watch v. United Kingdom’ (13 September 2018), para 112.

Take, for instance, ‘intelligent’ video surveillance that is supposed to alert to pickpocketing at train stations. Certainly, it will need to indiscriminately record at least some minutes of what happens on the platform to distinguish suspicious behavior from people just strolling around waiting for their trains.

Additionally, the data will be needed to train new algorithms and evaluate algorithms which are already applied, cf. CJEU ‘Passenger Name Record’ (see note 70), para 198.

‘[N]ot […] limited to what is strictly necessary’, CJEU ‘Passenger Name Record’ (see note 70), para 206.

CJEU ‘Passenger Name Record’ (see note 70), paras 204–209.

Meaning that the ‘models and criteria’ applied by Canada must be ‘specific and reliable, making it possible […] to arrive at results targeting individuals who might be under a “reasonable suspicion” of participation in terrorist offences or serious transnational crime’, cf. CJEU ‘Passenger Name Record’ (see note 70), para 172.

See CJEU ‘Passenger Name Record’ (see note 70): ‘The transfer of that data to Canada is to take place regardless of whether there is any objective evidence permitting the inference that the passengers are liable to present a risk to public security in Canada.’ [para 186] ‘[T]hat processing is intended to identify the risk to public security that persons, who are not, at that stage, known to the competent services, may potentially present, and who may, on account of that risk, be subject to further examination. In that respect, the automated processing of that data, before the arrival of the passengers in Canada, facilitates and expedites security checks, in particular at borders.’ [para 187] According to para 191 et seq., that suffices to ‘establish a connection between the personal data to be retained and the objective pursued’. Cf. Rademacher ( 2017 ), pp. 412–413.

That is especially true as the CJEU’s reasoning in its ‘Passenger Name Record’ decision (see note 70) is premised on public international law concerning air traffic and respective border controls, cf. para 188. However, public international law is only one line of argument in favor of the agreement the Court has accepted, cf. para 187.

Due to the interference with the fundamental right to informational self-determination, in any case a specific legal basis is required, see Article 52(1) CFR.

See also CJEU ‘Ministerio Fiscal’ (see note 68), paras 54, 56–57.

Ferguson ( 2017 ), p. 98; ditto Wittmann ( 2014 ), pp. 368–369. See also Joh ( 2016 ), p. 17: ‘Unlike arrests or wiretaps, the decision to focus police attention on a particular person, without more, is unlikely to be considered a Fourth Amendment event.’

Cf. Ferguson ( 2014 ), p. 1333: ‘In a government truly of limited powers, police would not have the surveillance powers to invade privacy or security unless there was a law specifically allowing it. Such is not the current reality under the Fourth Amendment.’ But cf. Ferguson ( 2017 ), p. 116, suggesting that social media monitoring and respective storage of data could interfere with the First Amendment as well.

Katz v. United States, 389 U.S. 347 (1967), p. 361 (Harlan, J., concurring).

‘Public’ is defined quite broadly, encompassing any communication that is directed to third parties, cf. Smith v. Maryland, 442 U.S. 735 (1979); for an in-depth analysis of the Court’s case law see Wittmann ( 2014 ), pp. 146–328; arguably, Carpenter v. United States has destabilized the third party doctrine, too (cf. note 99).

Joh ( 2016 ), p. 18, notes that ‘[s]urprisingly, there is little discussion of these decisions that the police make about individuals before any search, detention, or arrest takes place. Rather, current unresolved issues of police technology have focused on whether a particular use is a Fourth Amendment search requiring a warrant and probable cause.’

Rich ( 2016 ), pp. 895–901; Ferguson ( 2015 ), pp. 388–409; Joh ( 2014 ), pp. 55–65, but see pp. 66–67: ‘Beyond the Fourth Amendment’.

Cf. Kyllo v. United States, 533 U.S. 27 (2001), pp. 34–35.

That does not mean that legislators could not step in and implement more restrictive requirements for surveillance that fall short of constituting an interference under the Fourth Amendment. According to Ferguson ( 2017 ), p. 101, however, such legislative restrictions or clarifications are, to date, missing. The new California Consumer Privacy Act (CCPA), which has received great attention in the EU, too, limits its scope of application to data processing by private entities (cf. Cal. Civ. Code § 1798.140(c)).

Cf. Ferguson ( 2014 ), p. 1305, identifying seven ‘values’ discussed as underlying the Fourth Amendment case law.

Ferguson ( 2014 ), p. 1307.

United States v. Jones, 565 U.S. 400 (2012), p. 404.

Cf. Ferguson ( 2014 ), p. 1308.

United States v. Jones (see note 90), p. 418 (Alito, J., concurring). But see also Justice Sotomayor’s opinion, ibid, at p. 414, consenting to the majority that ‘the Government’s physical intrusion on Jones’ Jeep supplies a […] basis for decision’ in any case.

A similarly historical approach applies to surveillance technology that is able to ‘explore details of the home that would previously have been unknowable without physical intrusion’ (Kyllo v. United States (see note 86), p. 40—such surveillance does constitute a search within the meaning of the Fourth Amendment; see also Florida v. Jardines, 133 S.Ct. 1409 (2013), p. 1419 (Kagan, J., concurring)).

United States v. Jones (see note 90), p. 430 (Alito, J., concurring).

Ibid, pp. 429–430 (Alito, J., concurring).

Ibid, pp. 416–417 (Sotomayor, J., concurring). See also paras. 18 and 42. Cf. Staben ( 2016 ), pp. 67–68: the argument of chilling effects does appear in the Supreme Court’s jurisprudence, but usually regarding the First Amendment.

Thus, at least under traditional interpretation of the Fourth Amendment, constituting public disclosure; see also notes 83 and 99.

The crime in question in Carpenter was robbery. Interestingly, Justice Alito dissented, arguing that the majority’s decision would be ‘revolutionary’ inasmuch as it ignored established case law according to which the Fourth Amendment would not apply to ‘an order merely requiring a [third] party to look through its own records and produce specific documents’ (Carpenter v. United States, 585 U.S. ____ (2018), p. 12 (Roberts, C. J., for the majority).

Ibid, p. 18 (Roberts, C. J., for the majority).

Obviously, the degree of precision and accuracy that is required will vary depending on the intrusiveness of the surveillance measure itself, the availability of less intrusive means, and the severity of the crime or threat in question.

It is a mistake to conclude that the application of machine learned patterns could not result in individualized predictions (cf. Ferguson 2017 , p. 127: ‘generalized suspicion’). As soon as data concerning a specific individual is used as input data for the prediction, the result is individualized by definition . The question that really matters is, whether it is individualized enough , i.e. whether the pattern in question relies on more than just one or two predictors such as place of birth, education etc. (cf. Hermstrüwer, para 10, and also paras 30–34 on the converse risk of excessive individualization (‘overfitting’)). One should bear in mind that all police suspicion, be it detected by human or technological means, starts with and relies on some form of pattern recognition, i.e. the application of previously learned information to new situations. For details cf. Rademacher ( 2017 ), pp. 373–377, 381–383, and Harcourt and Meares ( 2011 ), p. 813: ‘In reality, most individuals arouse suspicion because of the group-based-type behavior that they exhibit or the fact that they belong to readily identifiable groups—sex and age are two examples—rather than because of unique individual traits. Typically, individuals come to police attention because they are young, or are male, or are running away from the police, or have a bulge in their pocket’.

See, for further details on the methods of evaluating predictive algorithms and on the difference between precision and accuracy, Degeling and Berendt ( 2017 ), esp. 3.2.

All forms of suspicion are probabilistic in nature, be it human or technological. By definition, reliance on ‘suspicion’ accepts that any actions based thereon are made in a state of possible incompleteness of information (cf. Rich 2016 , p. 898; Rademacher 2017 , p. 383) and should—consequently—be open to ex post rectification.

Interestingly, American scholars suggest comparing smart law enforcement to drug dogs rather than to humans, e.g. Rich ( 2016 ), pp. 913–921. On the law of drug dogs see esp. Rodriguez v. United States, 575 U.S. __ (2015), pp. 5–6 (Ginsburg, J., for the majority), finding that police may perform investigations (like dog sniffs) unrelated to a roadside detention (which itself requires ‘probable cause’ under the Fourth Amendment), but only if that investigation does not prolong the stop. In Florida v. Jardines (see note 93), the Supreme Court held, in a 5 to 4 decision, that a dog sniff does amount to a search within the meaning of the Fourth Amendment when it is performed on property surrounding the home of a person (so-called curtilage, in that specific case: a front porch), if that property had been entered with the intention of performing that investigation. On the other hand, Justice Scalia reaffirmed that ‘law enforcement officers need not “shield their eyes” when passing by the home “on public thoroughfares”’ (at p. 1423).

Ferguson ( 2017 ), p. 198: ‘“Here is how we test it” may be a more comforting and enlightening answer than “here is how it works.”’ For a detailed analysis of up-to-date testing mechanisms cf. Kroll et al. ( 2017 ), pp. 643–656.

Cf. Rich ( 2016 ), pp. 913–921, premised on the comparability of smart law enforcement (‘automated suspicion algorithms’) with drug dogs.

Cf. Hermstrüwer, paras 52–55, who correctly notes that acceptability of false positives or false negatives depends on whether AI is applied for information gathering, or for preventive or punitive purposes.

Cf. Kroll et al. ( 2017 ), pp. 695–705, for detailed ‘recommendations’ to lawmakers, policymakers, and computer scientists to ‘foster’ interdisciplinary collaboration.

Ibid, pp. 657–658.

See, e.g., Bieker et al. ( 2018 ), p. 610, referring to Article 13 of the EU’s General Data Protection Regulation (see para 69).

Ditto see Hermstrüwer, paras 3, 45–47, and Kroll et al. ( 2017 ), p. 657: a ‘naïve solution to the problem’; Ferguson ( 2017 ), pp. 137, 138: ‘The issue […] is not the transparency of the algorithm […] but the transparency of how the program is explained to the public and, of course, what is done with the information’. See also Wischmeyer, passim, and esp. paras 24 et seq. and 30.

Cf. Joh ( 2014 ), pp. 50–55.

Cf. Rich ( 2016 ), p. 919.

Hildebrandt ( 2016 ), pp. 3, 21–22; see also Marks et al. ( 2017 ), pp. 714–715: ‘automatic criminal justice’.

On the need to (re)establish human agency cf. Wischmeyer, paras 24 et seq.

See also Kroll et al. ( 2017 ), pp. 657–660, Ferguson ( 2017 ), pp. 137–138, and, for an up-to-date overview of the accountablity discussion, Andrews ( 2019 ). This is not to say that specific public officials should not have the right to scrutinize source codes, training and test data etc. if circumstances, especially procedures of judicial review require that kind of additional transparency. See for details Wischmeyer, esp. para 47.

For techniques to preserve privacy in the course of human re-evaluation of video data cf. Birnstill et al. ( 2015 ).

Ditto Rich ( 2016 ), p. 920, who, however, appears to be skeptical as to the practicality of such systems of disclosure: ‘theoretically solvable’. See also Wischmeyer, esp. para 27. The respective techniques are called explainable AI (short form: XAI), cf. Waltl and Vogl ( 2018 ) and Samek et al. ( 2017 ).

Eventually also including ‘counterintuitive’ insights, cf. Ferguson ( 2017 ), pp. 117, 136–140; for a critical account under EU law cf. Rademacher ( 2017 ), pp. 388–391.

It is important to note that in this case it is irrelevant that the software itself is limited to detecting correlations and is not able to ‘understand’ casual links. To Ferguson ( 2017 ), p. 119, the difference between correlation and causation is one of the ‘fundamental questions’ behind big data policing. I disagree: The lack of understanding, which is inherent in machine learning, would only constitute a case against the use of smart law enforcement technologies, if we were to require the software to be held accountable, i.e. require it to explain itself and be subject, eventually, to disciplinary or electoral sanctions. Instead, what we need, is to establish a regulatory framework that preserves human accountability. Therefore, the software itself does not need to ‘understand’ the correlations it searches for. See also, from a private law perspective, Eidenmüller ( 2017 ), p. 13: ‘Treating robots like humans would dehumanize humans, and therefore we should refrain from adopting this policy.’

Cf. Rich ( 2016 ), pp. 911–924 for a detailed analysis of the law on drug dogs (in the US) and its suitability for being applied, by way of analogy, to ‘automated suspicion algorithms’; see also note 105.

Ferguson ( 2017 ), p. 133.

Cf. Tischbirek, paras 5 et seq.

See Buchholtz, para 30; Hacker ( 2018 ), pp. 1143–1144; but see also Brantingham et al. ( 2018 ), p. 1: ‘We find that there were no significant differences […] by racial-ethnic group between the control and treatment conditions.’ For a detailed account of comparable software being tested in the criminal justice system of the UK, cf. Scantamburlo et al. ( 2019 ), esp. pp. 58 et seq.

See Hermstrüwer, paras 3–4; see also Bennet Capers ( 2017 ), pp. 1242, 1271: ‘I am a black man. […] I am interested in technology that will lay bare not only the truth of how we police now but also how those of us who are black or brown live now.’

The example is taken from Bennet Capers ( 2017 ), p. 1242, who uses this equation to describe his perception of the status quo of human law enforcement in the US.

Ditto Hacker ( 2018 ), pp. 1146–1150.

Cf. Kroll et al. ( 2017 ), p. 685; see Tischbirek, para 13. For an up-to-date catalogue of sensitive predictors under EU law see Article 21 CFR.

Ferguson ( 2017 ), pp. 122–124; Kroll ( 2017 ), p. 685; see Tischbirek, paras 11–12.

Current research is summed up by Kroll et al. ( 2017 ), pp. 682–692. For an account of legal tools to reveal discriminatory algorithms see Hacker ( 2018 ), pp. 1170–1183.

Kroll et al. ( 2017 ), p. 674 (procedural fairness), pp. 690–692 (nondiscrimination). See also Hermstrüwer, paras 41–43.

The approach has therefore been labelled ‘fairness through awareness’, cf. Dwork et al. ( 2011 ). See also Tischbirek, paras 31 et seq.: ‘towards a paradigm of knowledge creation’.

Ferguson ( 2017 ), p. 137.

See, e.g., Bennet Capers ( 2017 ), pp. 1268–1283, 1285, with a strong plea for the replacement of (biased) human policing by (hopefully) less biased policing by technology.

Cf. Tyler ( 1990 ), pp. 3–4; Hoffmann-Riem ( 2017 ), pp. 33–34.

See, e.g., Cheng ( 2006 ), pp. 659 et seq.

See note 4 and Rich ( 2013 ), pp. 802–804; for more recent reflections on impossibility structures or ‘embedded law’ cf. Rademacher ( 2019 ) and Becker ( 2019 ), respectively.

See, for a plea for preventive regulation of financial markets, Schemmel, para 46.

Cf. Bennet-Capers ( 2017 ), pp. 1282–1283, 1285–1291.

Orwell ( 1949 ).

Cf. Oganesian and Heermann ( 2018 ).

For a more sophisticated attempt to explain ‘The Dangers of Surveillance’ cf. Richards ( 2013 ), esp. pp. 1950–1958, 1962–1964. See Timan et al. ( 2018 ), pp. 744–748, for an interdisciplinary attempt to reconcile the insights of ‘surveillance studies’ with legal reasoning, quite rightly asking for ‘more legal scholarship’ that ‘views surveillance as generally good and bad at the same time, or as good or bad depending on the situation’.

Solove ( 2007 ), pp. 758, 765; Richards ( 2013 ), p. 1961; for a thorough analysis from the German perspective see Staben ( 2016 ) and Oermann and Staben ( 2013 ).

Joh ( 2019 ), p. 178: ‘[A]s cities become “smarter”, they increasingly embed policing itself into the urban infrastructure.’

See also Timan et al. ( 2018 ), p. 738: ‘increasing blend of governmental and corporate surveillance infrastructures’ and ‘an increase of citizen-instigated forms of surveillance can be witnessed’.

Cf. Rich ( 2013 ), p. 810. This sentiment might actually deserve some form of legal, perhaps even constitutional recognition. The reason for that is that we live in what I would call ‘imperfect democracies’. I.e. in societies that try very hard to balance out majority rule on the one hand and the individual’s rights to self-determination and political participation on the other hand—by providing a plethora of fundamental and political rights in order to protect minorities—but which fail, and will continue to fail in the future, to fully provide such balance for many practical reasons. So as long as even democratic laws cannot claim to be perfectly legitimate with regard to each and every paragraph , there is good reason to argue that such laws on their part may not claim perfect compliance.

Petroski ( 2018 ).

Cheng ( 2006 ), pp. 682–688, with a critical account of legislation respecting that wish.

See also Hartzog et al. ( 2015 ), esp. pp. 1778–1792, advocating for automated law enforcement to be consciously ‘inefficient’ to prevent ‘perfect enforcement’.

Mulligan ( 2008 ), p. 3.

See also Timan et al. ( 2018 ), p. 747, citing J Cohen: ‘importance of room for play’.

United States v. Jones (see note 90), p. 429; see also note 55 for the German discussion.

See also Rich ( 2013 ), pp. 804–828; Hartzog et al. ( 2015 ), pp. 1786–1793; Hoffmann-Riem ( 2017 ), p. 34; Rademacher ( 2017 ), pp. 398, 403–410.

Algorithm Watch, BertelsmannStiftung (2019) Automating Society. Taking stock of automated decision-making in the EU. www.bertelsmann-stiftung.de/de/publikationen/publikation/did/automating-society . Accessed 21 Feb 2019

Andrews L (2019) Algorithms, regulation, and governance readiness. In: Yeung K, Lodge M (eds) Algorithmic regulation. Oxford University Press, Oxford, pp 203–223

Chapter Google Scholar

Associated Press (2018) Israel claims 200 attacks predicted, prevented with data tech. CBS News. www.cbsnews.com/news/israel-data-algorithms-predict-terrorism-palestinians-privacy-civil-liberties . Accessed 29 Nov 2018

Bachmeier L (2018) Countering terrorism: suspects without suspicion and (pre-)suspects under surveillance. In: Sieber U, Mitsilegas V, Mylonopoulos C, Billis E, Knust N (eds) Alternative systems of crime control. Duncker&Humblodt, Berlin, pp 171–191

Google Scholar

Barret B (2016) New surveillance system may let cops use all of the cameras. Wired. www.wired.com/2016/05/new-surveillance-system-let-cops-use-cameras . Accessed 16 Nov 2018

Becker M (2019) Von der Freiheit, rechtswidrig handeln zu können. Zeitschrift für Urheber- und Medienrecht 64:636–648

Bieker F, Bremert B, Hansen M (2018) Verantwortlichkeit und Einsatz von Algorithmen bei öffentlichen Stellen. Datenschutz und Datensicherheit:608–612

Bier W, Spiecker gen Döhmann I (2012) Intelligente Videoüberwachungstechnik: Schreckensszenario oder Gewinn für Datenschutz. Computer und Recht:610–618

Big Brother Watch (2018) Face off. The lawless growth of facial recognition in UK policing. https://www.bigbrotherwatch.org.uk/wp-content/uploads/2018/05/Face-Off-final-digital-1.pdf . Accessed 29 Nov 2018

Birnstill P, Ren D, Beyerer J (2015) A user study on anonymization techniques for smart video surveillance. IEEE. https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=7301805 . Accessed 29 Nov 2018

Böckenförde T (2008) Auf dem Weg zur elektronischen Privatsphäre. JuristenZeitung 63:925–939

Bouachir W, Gouiaa R, Li B, Noumeir R (2018) Intelligent video surveillance for real-time detection of suicide attempts. Pattern Recogn Lett 110:1–7

Article Google Scholar

Brantingham PJ, Valasik M, Mohler GO (2018) Does predictive policing lead to biased arrests? Results from a randomized controlled trial. Stat Public Policy 5:1–6

Brühl J (2018) Wo die Polizei alles sieht. Süddeutsche Zeitung. www.sueddeutsche.de/digital/palantir-in-deutschland-wo-die-polizei-alles-sieht-1.4173809 . Accessed 26 Nov 2018

Bundesministerium des Innern (2016) ‘Polizei 2020’. www.bmi.bund.de/DE/themen/sicherheit/nationale-und-internationale-zusammenarbeit/polizei-2020/polizei-2020-node.html . Accessed 7 Dec 2018

Bundesministerium des Innern (2018) Pressemitteilung. Projekt zur Gesichtserkennung erfolgreich. www.bmi.bund.de/SharedDocs/pressemitteilungen/DE/2018/10/gesichtserkennung-suedkreuz.html . Accessed 29 Nov 2018

Bundespolizeipräsidium (2018) Teilprojekt 1 ‘Biometrische Gesichtserkennung’. Abschlussbericht. www.bundespolizei.de/Web/DE/04Aktuelles/01Meldungen/2018/10/181011_abschlussbericht_gesichtserkennung_down.pdf;jsessionid=2A4205E1606AC617C8006E65DEDD7D22.2_cid324?__blob=publicationFile&v=1 . Accessed 29 Nov 2018

Bundestag (2018) Antwort der Bundesregiegung auf die Kleine Anfrage ‘Umsetzung der EU-Richtlinie zur Vorratsdatenspeicherung von Fluggastdaten’. BT-Drucksache 19/4755

Burkert H (2012) Balancing informational power by information power, or rereading Montesquieu in the internet age. In: Brousseau E, Marzouki M, Méadel C (eds) Governance, regulations and powers on the internet. CUP, Cambridge, pp 93–111

Candamo J, Shreve M, Goldgof D, Sapper D, Kasturi R (2010) Understanding transit scenes: a survey on human behavior recognition algorithms. IEEE Trans Intell Transp Syst 11:206–224

Capers IB (2017) Race, policing, and technology. N C Law Rev 95:1241–1292

Caplan J, Kennedy L (2016) Risk terrain modeling. University of California Press, Oakland

Book Google Scholar

Chaos Computer Club (2018) Biometrische Videoüberwachung: Der Südkreuz-Versuch war kein Erfolg. www.ccc.de/de/updates/2018/debakel-am-suedkreuz . Accessed 26 Nov 2018

Cheng E (2006) Structural laws and the puzzle of regulating behavior. Northwest Univ School Law 100:655–718

Davenport T (2016) How Big Data is helping the NYPD solve crime faster. Fortune. fortune.com/2016/07/17/big-data-nypd-situational-awareness . Accessed 29 Nov 2018

Degeling M, Berendt B (2017) What is wrong about Robocops as consultants? A technology-centric critique of predictive policing. AI & Soc 33:347–356

Demetis D (2018) Fighting money laundering with technology: a case study of Bank X in the UK. Decis Support Syst 105:96–107

Dimitrova D (2018) Data protection within police and judicial cooperation. In: Hofmann HCH, Rowe GC, Türk AH (eds) Specialized administrative law of the European Union. Oxford University Press, Oxford, pp 204–233

Dwork C, Hardt M, Pitassi T, Reingold O, Zemel R (2011) Fairness through awareness. arXiv.org/pdf/1104.3913.pdf . Accessed 29 Nov 2017

Eidenmüller H (2017) The rise of robots and the law of humans. Oxford Legal Studies Paper. Ssrn.com/abstract=2941001 . Accessed 29 Nov 2018

Eubanks V (2018) A child abuse prediction model fails poor families. Wired. www.wired.com/story/excerpt-from-automating-inequality . Accessed 29 Nov 2018

Ferguson AG (2014) Fourth amendment security in public. William Mary Law Rev 55:1283–1364

Ferguson AG (2015) Big data and predictive reasonable suspicion. Univ Pa Law Rev 163:327–410

Ferguson AG (2017) The rise of big data policing. New York University Press, New York

Hacker P (2018) Teaching fairness to artificial intelligence: existing and novel strategies against algorithmic discrimination under EU law. Common Mark Law Rev 55:1143–1186

Harcourt B, Meares T (2011) Randomization and the fourth amendment. U Chi L Rev 78:809–877

Hartzog W, Conti G, Nelson J, Shay LA (2015) Inefficiently automated law enforcement. Mich State Law Rev:1763–1796

Heinemann M (2015) Grundrechtlicher Schutz informationstechnischer Systeme. Schriften zum Öffentlichen Recht, vol 1304. Duncker & Humblot, Berlin

Henderson SE (2016) Fourth amendment time machines (and what they might say about police body cameras). J Constit Law 18:933–973

Hildebrandt M (2016) Law as information in the era of data-driven agency. Mod Law Rev 79:1–30

Hoffmann-Riem W (2008) Der grundrechtliche Schutz der Vertraulichkeit und Integrität eigengenutzer informationstechnischer Systeme. JuristenZeitung 63:1009–1022

Hoffmann-Riem W (2017) Verhaltenssteuerung durch Algorithmen – Eine Herausforderung für das Recht. Archiv des öffentlichen Rechts 142:1–42

Joh E (2014) Policing by numbers: big data and the fourth amendment. Wash Law Rev 89:35–68

Joh E (2016) The new surveillance discretion: automated suspicion, big data, and policing. Harv Law Policy Rev 10:15–42

Joh E (2019) Policing the smart city. International Journal of Law in Context 15:177–182

Kroll JA, Huey J, Barocas S, Felten EW, Reidenberg JR, Robinson DG, Yu H (2017) Accountable algorithms. Univ Pa Law Rev 165:633–705

Lismont J, Cardinaels E, Bruynseels L, De Goote S, Baesens B, Lemahieu W, Vanthienen J (2018) Predicting tax avoidance by means of social network analytics. Decis Support Syst 108:13–24

Marks A, Bowling B, Keenan C (2017) Automatic justice? In: Brownsword R, Scotford E, Yeung K (eds) The Oxford handbook of law, regulation, and technology. Oxford University Press, Oxford, pp 705–730

Marsch N (2012) Die objektive Funktion der Verfassungsbeschwerde in der Rechtsprechung des Bundesverfassungsgerichts. Archiv des öffentlichen Rechts 137:592–624

Marsch N (2018) Das europäische Datenschutzgrundrecht. Mohr Siebeck, Tübingen

May J (2018) Drug enforcement agency turns to A.I. to help sniff out doping athletes. Digital trends. https://www.digitaltrends.com/outdoors/wada-artificial-intelligence-doping-athletes. Accessed 23 Oct 2019

McKay S (2015) Covert policing. Oxford University Press, Oxford

Mulligan CM (2008) Perfect enforcement of law: when to limit and when to use technology. Richmond J Law Technol 14:1–49

Murphy E (2007) The new forensics: criminal justice, false certainty, and the second generation of scientific evidence. Calif Law Rev 95:721–797

Oermann M, Staben J (2013) Mittelbare Grundrechtseingriffe durch Abschreckung? DER STAAT 52:630–661

Oganesian C, Heermann Th (2018) China: Der durchleuchtete Mensch – Das chinesische Socia-Credit-System. ZD-Aktuell:06124

Oldiges M (1987) Einheit der Verwaltung als Rechtsproblem. Neue Zeitschrift für Verwaltungsrecht 1987:737–744

Orwell G (1949) Nineteen eighty-four. Secker & Warburg, London

Pelzer R (2018) Policing terrorism using data from social media. Eur J Secur Res 3:163–179

Petroski W (2018) Iowa Senate OKs ban on traffic enforcement cameras as foes predict more traffic deaths. Des Moines Register. https://eu.desmoinesregister.com/story/news/politics/2018/02/27/traffic-enforcementcameras-banned-under-bill-passed-iowa-senate/357336002 . Accessed 23 Oct 2019

Poscher R (2017) The right to data protection. In: Miller R (ed) Privacy and power. Cambridge University Press, Cambridge, pp 129–141

Rademacher T (2017) Predictive Policing im deutschen Polizeirecht. Archiv des öffentlichen Rechts 142:366–416

Rademacher T (2019) Wenn neue Technologien altes Recht durchsetzen: Dürfen wir es unmöglich machen, rechtswidrig zu handeln? JuristenZeitung 74:702–710

Reidenberg J (1998) Lex Informatica: the formulation of information policy rules through technology. Tex Law Rev 76:553–593

Rich M (2013) Should we make crime impossible? Harv J Law Public Policy 36:795–848

Rich M (2016) Machine learning, automated suspicion algorithms, and the fourth amendment. Univ Pa Law Rev 164:871–929

Richards NM (2013) The dangers of surveillance. Harv Law Rev 126:1934–1965

Rosenthal D (2011) Assessing digital preemption (and the future of law enforcement?). New Crim Law Rev 14:576–610

RWI Essen (2018) “Erfolgreiche” Gesichtserkennung mit hunderttausend Fehlalarmen. http://www.rwi-essen.de/unstatistik/84 . Accessed 29 Nov 2018

Samek W, Wiegand T, Müller K-R (2017) Explainable artificial intelligence: understanding, visualizing and interpreting deep learning models. arXiv:1708.08296v1. Accessed 25 Oct 2019

Saracco R (2017) An artificial intelligence ‘nose’ to sniff diseases: EIT Digital. https://www.eitdigital.eu/newsroom/blog/article/an-artificial-intelligence-nose-to-sniff-diseases . Accessed 29 Nov 2018

Saunders J, Hunt P, Hollywood JS (2016) Predictions put into practice: a quasi-experimental evaluation of Chicago’s predictive policing pilot. J Exp Criminol 12:347–371

Scantamburlo T, Charlesworth A, Cristianini N (2019) Machine decisions and human consequences. In: Yeung K, Lodge M (eds) Algorithmic regulation. Oxford University Press, Oxford, pp 49–81

Schlossberg T (2015) New York police begin using ShotSpotter system to detect gunshots. The New York Times. https://www.nytimes.com/2015/03/17/nyregion/shotspotter-detection-system-pinpoints-gunshot-locations-and-sends-data-to-the-police.html . Accessed 29 Nov 2018

Seidensticker K, Bode F, Stoffel F (2018) Predictive policing in Germany. http://nbn-resolving.de/urn:nbn:de:bsz:352-2-14sbvox1ik0z06 . Accessed 29 Nov 2018

Singelnstein T (2018) Predictive Policing: Algorithmenbasierte Straftatenprognosen zur vorausschauenden Kriminalintervention. Neue Zeitschrift für Strafrecht:1–9

Solove DJ (2007) ‘I’ve got nothing to hide’ and other misunderstandings of privacy. San Diego Law Rev 44:745–772

Spice B (2015) Carnegie Mellon developing online tool to detect and identify sex traffickers. www.cmu.edu/news/stories/archives/2015/january/detecting-sex-traffickers.html . Accessed 24 Oct 2019

Staben J (2016) Der Abschreckungseffekt auf die Grundrechtsausübung. Internet und Gesellschaft, vol 6. Mohr Siebeck, Tübingen

Thomas S, Gupta S, Subramanian V (2017) Smart surveillance based on video summarization. IEEE. https://ieeexplore.ieee.org/document/8070003 . Accessed 29 Nov 2018

Throckmorton CS, Mayew WJ, Venkatachalam M, Collins LM (2015) Financial fraud detection using vocal, linguistic and financial cues. Decis Support Syst 74:78–87

Timan T, Galic M, Koops B-J (2018) Surveillance theory and its implications for law. In: Brownsword R, Scotford E, Yeung K (eds) The Oxford handbook of law, regulation, and technology. Oxford University Press, Oxford, pp 731–753

Trute HH (2009) Grenzen des präventionsorientierten Polizeirechts in der Rechtsprechung des Bundesverfassungsgerichts. Die Verwaltung 42:85–104

Tyler TR (1990) Why people obey the law. Yale University Press, New Haven and London

Waltl B, Vogl R (2018) Increasing transparency in algorithmic decision-making with explainable AI. Datenschutz und Datensicherheit:613–617

Wendt K (2018) Zunehmender Einsatz intelligenter Videoüberwachung. ZD-Aktuell:06122

Wittmann P (2014) Der Schutz der Privatsphäre vor staatlichen Überwachungsmaßnahmen durch die US-amerikanische Bundesverfassung. Nomos, Baden-Baden

Wysk P (2018) Tausche Freiheit gegen Sicherheit? Die polizeiliche Videoüberwachung im Visier des Datenschutzrechts. Verwaltungsarchiv 109:141–162

Zetter K (2012) Public buses across country quietly adding microphones to record passenger conversations. Wired. www.wired.com/2012/12/public-bus-audio-surveillance . Accessed 26 Nov 2018

Download references

Author information

Authors and affiliations.

Faculty of Law, University of Hannover, Hannover, Germany

Timo Rademacher

You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Timo Rademacher .

Editor information

Editors and affiliations.

Faculty of Law, University of Bielefeld, Bielefeld, Germany

Thomas Wischmeyer

Rights and permissions

Reprints and permissions

Copyright information

About this chapter

Rademacher, T. (2020). Artificial Intelligence and Law Enforcement. In: Wischmeyer, T., Rademacher, T. (eds) Regulating Artificial Intelligence. Springer, Cham. https://doi.org/10.1007/978-3-030-32361-5_10

Download citation

DOI : https://doi.org/10.1007/978-3-030-32361-5_10

Published : 30 November 2019

Publisher Name : Springer, Cham

Print ISBN : 978-3-030-32360-8

Online ISBN : 978-3-030-32361-5

eBook Packages : Law and Criminology Law and Criminology (R0)

Share this chapter

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

Publish with us

Policies and ethics

Find a journal
Track your research

Police surveillance and facial recognition: Why data privacy is imperative for communities of color

Subscribe to the center for technology innovation newsletter, nicol turner lee and nicol turner lee senior fellow - governance studies , director - center for technology innovation @drturnerlee caitlin chin-rothmann caitlin chin-rothmann fellow - center for strategic and international studies, former research analyst - the brookings institution @caitlintchin.

Tuesday April 12, 2022

70 min read

This paper was originally presented at the American Bar Association’s Antitrust Spring Meeting on April 8, 2022, in Washington, D.C.

Introduction

Governments and private companies have a long history of collecting data from civilians, often justifying the resulting loss of privacy in the name of national security, economic stability, or other societal benefits. But it is important to note that these trade-offs do not affect all individuals equally. In fact, surveillance and data collection have disproportionately affected communities of color under both past and current circumstances and political regimes.

From the historical surveillance of civil rights leaders by the Federal Bureau of Investigation (FBI) to the current misuse of facial recognition technologies, surveillance patterns often reflect existing societal biases and build upon harmful and virtuous cycles. Facial recognition and other surveillance technologies also enable more precise discrimination, especially as law enforcement agencies continue to make misinformed, predictive decisions around arrest and detainment that disproportionately impact marginalized populations.

In this paper, we present the case for stronger federal privacy protections with proscriptive guardrails for the public and private sectors to mitigate the high risks that are associated with the development and procurement of surveillance technologies. We also discuss the role of federal agencies in addressing the purposes and uses of facial recognition and other monitoring tools under their jurisdiction, as well as increased training for state and local law enforcement agencies to prevent the unfair or inaccurate profiling of people of color. We conclude the paper with a series of proposals that lean either toward clear restrictions on the use of surveillance technologies in certain contexts, or greater accountability and oversight mechanisms, including audits, policy interventions, and more inclusive technical designs.

The history of race and surveillance in the United States

The oversurveillance of communities of color dates back decades to the civil rights movement and beyond. During the 1950s and 1960s, the FBI tracked Martin Luther King, Jr., Malcolm X, and other civil rights activists through its Racial Matters and COINTELPRO programs, without clear guardrails to prevent the agency from collecting intimate details about home life and relationships that were unrelated to law enforcement. 1 More recently, the Black Lives Matter (BLM) movement, initially sparked in 2013 after the murder of 17-year-old Trayvon Martin by a local vigilante, has highlighted racial biases in policing that disproportionately lead to unwarranted deaths, improper arrests, and the excessive use of force against Black individuals. 2 Over the years, the government’s response to public protests over egregious policing patterns has raised various concerns over the appropriate use of surveillance, especially when primarily focused on communities of color. In 2015, the Baltimore Police Department reportedly used aerial surveillance, location tracking, and facial recognition to identify individuals who publicly protested the death of Freddie Gray. 3 Similarly, after George Floyd was murdered in 2020, the U.S. Department of Homeland Security (DHS) deployed drones and helicopters to survey the subsequent protests in at least 15 cities. 4

But African Americans are not the only population that has been subjected to overt tracking and profiling. The consequences of mass government surveillance were evident in programs like the China Initiative, which the Department of Justice (DOJ) launched in 2018 to prevent espionage and intellectual property theft and formally ceased in February 2022. 5 Although the China Initiative aimed to address national security threats from the Chinese government, it manufactured wider distrust and racial profiling of Chinese American academics, including those who were U.S. citizens or who lacked ties with the Chinese Communist Party. It led to several false arrests, including those of Temple University professor Xi Xiaoxing, UCLA graduate student Guan Lei, University of Tennessee professor Anming Hu, and National Weather Service scientist Sherry Chen. 6 Like with other historically-disadvantaged populations, government surveillance of Asian Americans is not a new phenomenon. As an example, the U.S. government monitored the broader Japanese American community for years even prior to World War II, including by accessing private communications and bank accounts, and eventually used census data after 1941 to locate and detain 120,000 people in internment camps. 7

Demonstrating similar profiling of an entire community, the New York Police Department (NYPD) and Central Intelligence Agency (CIA) surveilled Muslim neighborhoods, restaurants, mosques, stores, and student groups for over six years after September 11, 2001, listening in on conversations, recording license plates, and taking videos. 8 Over a decade after 9/11, a 2017 Pew Research Center survey found that 18% of Muslim American respondents still experienced being “singled out by airport security.” 9 From 2015 to 2020, Freedom of Information Act (FOIA) records exposed over 75 complaints sparked by intrusive airport searches or Islamophobic comments from Transportation Security Administration (TSA) officers toward people who were perceived to be of Middle Eastern descent. 10 Both the NYPD’s “Demographic Unit” surveillance and TSA’s profiling of Muslim travelers are widely considered to be inaccurate and ineffective in preventing violent crime. 11 Moreover, Customs and Border Protection (CBP) has deployed planes, boats, and radios to track and identify people along the U.S.-Mexico border—continuing a long tradition of hostility toward immigrants, especially those from Latino communities. Immigrant-focused surveillance extends far beyond a physical border; during the Obama and Trump administrations, Immigration and Customs Enforcement (ICE) purchased surveillance technology from private companies like Palantir and Thomson Reuters and used vehicle, insurance, tax, social media, and phone records to track undocumented immigrants throughout the country. 12 As early as 1992, the Drug Enforcement Administration surveilled phone call records to over 100 countries in bulk, which, over the years, may have gathered a significant amount of information from immigrants who called home to Mexico and countries in Central or South America. 13 In these and other cases, government entities directed surveillance with the stated goals of maintaining public order, preventing cyber theft, and protecting Americans more broadly—but the indiscriminate deployment and public vigilantism have contributed to and been fueled by deep-rooted discrimination that affects communities of color in the United States. In order to stop ongoing injustice, we need greater attention to this issue and concrete steps to protect personal privacy.

How law enforcement officers use facial recognition and other surveillance technologies

Although suspicion toward communities of color has historical roots that span decades, new developments like facial recognition technologies (FRT) and machine learning algorithms have drastically enlarged the precision and scope of potential surveillance. 14 Federal, state, and local law enforcement agencies often rely upon tools developed within the private sector, and, in certain cases, can access massive amounts of data either stored on private cloud servers or hardware (e.g., smartphones or hard drives) or available in public places like social media or online forums. 15 In particular, several government agencies have purchased access to precise geolocation history from data aggregators that compile information from smartphone apps or wearable devices. In the general absence of stronger privacy protections at the federal or state levels to account for such advancements in technology, enhanced forms of surveillance used by police officers pose significant risks to civilians already targeted in the criminal justice system and further the historical biases affecting communities of color. Next, we present tangible examples of how the private and public sectors both play a critical role in amplifying the reach of law enforcement through facial recognition and other surveillance technologies.

(A) Facial recognition

Facial recognition has become a commonplace tool for law enforcement officers at both the federal and municipal levels. Out of the approximately 42 federal agencies that employ law enforcement officers, the Government Accountability Office (GAO) discovered in 2021 that about 20, or half, used facial recognition. In 2016, Georgetown Law researchers estimated that approximately one out of four state and local law enforcement agencies had access to the technology. 16

On the procurement side, Clearview AI is one of the more prominent commercial providers of FRT to law enforcement agencies. Since 2017, it has scraped billions of publicly available images from websites like YouTube and Facebook, and enables customers to upload photos of individuals and automatically match them with other images and sources in the database. 17 As of 2021, the private startup had partnered with over 3,100 federal and local law enforcement agencies to identify people outside the scope of government databases. To put this tracking in perspective, the FBI only has about 640 million photos in its databases, compared to Clearview AI’s approximately 10 billion. 18

But Clearview AI is only one of numerous private companies that U.S. government agencies partner with to collect and process personal information. 19 Another example is Vigilant Solutions, which captures image and location information of license plates from billions of cars parked outside homes, stores, and office buildings, and which had sold access to its databases to approximately 3,000 local law enforcement agencies as of 2016. 20 Vigilant also markets various facial recognition products like FaceSearch to federal, state, and local law enforcement agencies; its customer base includes the DOJ and DHS, among others. 21 A third company, ODIN Intelligence, partners with police departments and local government agencies to maintain a database of individuals experiencing homelessness, using facial recognition to identify them and search for sensitive personal information such as age, arrest history, temporary housing history, and known associates. 22

In response to privacy and ethical concerns, and after the protests over George Floyd’s murder in 2020, some technology companies, including Amazon, Microsoft, and IBM, pledged to either temporarily or permanently stop selling facial recognition technologies to law enforcement agencies. 23 But voluntary and highly selective corporate moratoriums are insufficient to protect privacy, since they do not stop government agencies from procuring facial recognition software from other private companies. Moreover, a number of prominent companies have noticeably not taken this pledge or continue to either enable or allow scaping of their photos for third-party use in facial recognition databases. Furthermore, government agencies can still access industry-held data with varying degrees of due process—for example, although they would require a warrant with probable cause to compel precise geolocation data from first-party service providers in many cases, they might be able to access a person’s movement history without probable cause through other means, including by purchasing it from a data broker. 24

(B) Data aggregators and private sector information

The enormous scale of information that the private sector collects can feed into broader law enforcement efforts, since federal, state, and local government agencies have multiple channels by which to access corporate data. From January to June 2020 alone, federal, state, and local law enforcement agencies issued over 112,000 legal requests for data to Apple, Google, Facebook, and Microsoft—three times the number of requests than they submitted five years prior—of which approximately 85% were accommodated, including some subpoenas or court orders that did not require probable cause. 25 In 2020, reports surfaced that federal law enforcement agencies like the FBI, ICE, CBP, Drug Enforcement Agency, and the U.S. Special Operations Command purchased smartphone app geolocation data—without a warrant or binding court order—from analytics companies like Venntel, X-Mode, and Babel Street. 26 ICE and CBP used this data to enable potential deportations or arrests, which demonstrates how geolocation can have singular consequences for immigrant communities, especially among populations of color. 27

Although geolocation tracking is almost ubiquitous among smartphone apps, it also poses unique potential for harm—both since it enables the physical pursuit of an individual and because it allows entities to deduce extraneous details like sexual orientation, religion, health, or personal relationships from their whereabouts.

Law enforcement has also worked with commercial data aggregators to scan social media websites for photos and posts. In 2018, ICE used photos and status updates posted on Facebook to locate and arrest an immigrant using the pseudonym “Sid” in California—only one of thousands of individuals whom the agency reportedly tracks at any given point, aided by private data miners such as Giant Oak and Palantir. 28 On a local level, the Los Angeles Police Department reportedly pilot tested ABTShield, an algorithm developed by a Polish company, to scan millions of tweets from October to November 2020 for terms that included “protest,” “solidarity,” and “lives matter,” despite concerns that such bulk surveillance could pose privacy harms to BLM activists without presenting a clear benefit to public safety. 29

(C) Public-oriented and civilian surveillance

Technological advances have expanded government surveillance in traditionally “public” places, prompting legal questions over the boundaries between permissible or non-permissible data collection. For instance, the Electronic Frontier Foundation and University of Nevada estimate that over 1,000 local police departments fly drones over their communities. 30 The Chula Vista Police Department had dispatched drones for over 5,000 civilian calls as of March 2021, capturing images of individuals within public areas like sidewalks and parking lots. 31 Body-worn cameras, another common police resource, can function as an accountability safeguard in part as a response to BLM activism but also pose privacy concerns—particularly when videos of civilians in sensitive scenarios are retained for lengthy periods, used for facial recognition purposes, or even publicly posted online, or when bystanders in public areas are incidentally caught on camera. 32 Lastly, the everyday use of store-bought devices or apps by residents complicates the curtailment of excessive surveillance. Private sector apps, such as Neighbors (an Amazon subsidiary, and integrated with Amazon’s Ring video doorbell), NextDoor, and Citizen allow people to livestream, watch, and exchange opinions about potential crimes with other users in real-time, generating concerns over unconscious bias and privacy. 33 Surveillance cameras are becoming increasingly prevalent within private homes, restaurants, entertainment venues, and stores; hundreds of millions are estimated to operate smart security devices worldwide, some of which—such as Google Nest’s Doorbell and the Arlo Essential Wired Video Doorbell—include built-in facial recognition capabilities. 34 Simultaneously, Amazon’s Ring has partnered with almost 2,000 local law enforcement agencies to facilitate a process for officers to ask Ring users to voluntarily turn over their video recordings without the explicit use of a warrant. 35

Facial recognition is perhaps the most daunting of them all

Mass surveillance affects all Americans through a wide suite of technologies—but facial recognition, which has become one of the most critical and commonly-used technologies, poses special risks of disparate impact for historically marginalized communities. In December 2020, the New York Times reported that Nijeer Parks, Robert Williams, and Michael Oliver—all Black men—were wrongfully arrested due to erroneous matches by facial recognition programs. 36 Recent studies demonstrate that these technical inaccuracies are systemic: in February 2018, MIT and then-Microsoft researchers Joy Buolamwini and Timnit Gebru published an analysis of three commercial algorithms developed by Microsoft, Face++, and IBM, finding that images of women with darker skin had misclassification rates of 20.8%-34.7%, compared to error rates of 0.0%-0.8% for men with lighter skin. 37 Buolamwini and Gebru also discovered bias in training datasets: 53.6%, 79.6%, and 86.2% of the images in the Adience, IJB-A, and PBB datasets respectively contained lighter-skinned individuals. In December 2019, the National Institute of Standards and Technology (NIST) published a study of 189 commercial facial recognition programs, finding that algorithms developed in the United States were significantly more likely to return false positives or negatives for Black, Asian, and Native American individuals compared to white individuals. 38 When disparate accuracy rates in facial recognition technology intersect with the effects of bias in certain policing practices, Black and other people of color are at greater risk of misidentification for a crime that they have no affiliation with.

Some companies have publicly announced unilateral actions to improve the accuracy of their facial recognition algorithms and diversity of their training datasets—but the scope and effectiveness of such efforts fluctuate across the enormous quantity and breadth of facial recognition vendors. 39 The question of accuracy is magnified when factoring in the general lack of transparency across the industry; companies are not legally required to allow third-party audits of their algorithms, and many either do not or selectively publish their processes and results. For example, Amazon chose not to submit its Rekognition algorithm for testing in NIST’s 2018 report—even though, at the time, it was still licensing the algorithm for use by law enforcement agencies and in other highly-sensitive contexts. 40 Clearview AI has not publicly disclosed its rates of false positives or negatives, and similarly has not voluntarily submitted its algorithm for testing by NIST or another third party. 41

The application of existing privacy and surveillance safeguards in the context of law enforcement

The U.S. government has long acknowledged that surveillance cannot be unlimited. There must be some safeguards to prevent any privacy abuses by the government or private entities, as a matter of fundamental rights. To that end, federal, state, and local governments have enshrined privacy values into law—in certain contexts—through layers of constitutional principles, limited statutes, and court cases. However, new technology significantly shifts the traditional balance between surveillance and civil liberties, and the existing patchwork of laws may not be enough to prevent the risks stemming from facial recognition and other technologies. 47 As such, it is necessary to take stock of existing privacy safeguards and identify areas of improvement. Samuel Warren and Louis Brandeis described this phenomenon in their famous 1890 Harvard Law Review article: “That the individual shall have full protection in person and in property is a principle as old as the common law; but it has been found necessary from time to time to define anew the exact nature and extent of such protection.” 48

(A) How the law addresses government surveillance

In the United States, privacy principles can trace their roots to the Constitution. 49 Although the Fourth Amendment prevents the government from conducting “unreasonable” searches without probable cause to obtain a warrant, law enforcement officers can still collect data through other means, such as by purchasing personal information from data brokers or collecting data in public places where people do not possess a “reasonable expectation of privacy.” 50 Yet, even the Supreme Court has acknowledged, in certain cases, that the amplifying effect of technology in surveillance may require an examination of Fourth Amendment limitations in public places. 51 Although police officers can physically search people’s vehicles subject to an arrest, the Court ruled in Riley v. California (2014) that they cannot search a person’s smartphone without a warrant—acknowledging that smartphones are “a pervasive and insistent part of daily life … unheard of ten years ago” and the modern scope of data collection “calls for a new balancing of law enforcement and privacy interests.” 52 Citing Riley, the Court held in Carpenter v. United States (2018) that the government would also require a warrant to compel cell phone service providers to turn over geolocation records, arguing that “seismic shifts in digital technology that made possible the tracking of not only Carpenter’s location but also everyone else’s.” 53 Despite the majority opinions in Riley and Carpenter, there are limitations to the Supreme Court’s ability to preserve privacy principles through judicial interpretation alone. In his dissent in Carpenter, then-Justice Anthony Kennedy wrote that the government’s access of cell phone location records does not constitute a search under the Fourth Amendment, and individuals do not have a reasonable expectation of privacy in records controlled by a cell phone company. In another case, Florida v. Riley (1989), the Supreme Court held that police officers could fly a helicopter 400 feet above a greenhouse without a search warrant—even if the interior of the building would not be visible without aerial surveillance—and that people do not have a reasonable expectation of privacy if other helicopters could legally fly at that height and observe the activity from a public airspace. 54 While the Supreme Court has heard several major cases on geolocation technologies, there is still legal and social uncertainty around surveillance technologies like facial recognition and drones, where judicial history is extremely limited, especially at the highest court. 55 One of the earliest court cases on facial recognition occurred in Lynch v. State (2018), when the First District Court of Appeal in Florida decided that a Black man named Willie Allen Lynch, who was identified by police through a facial recognition program, was not legally entitled to view the other four erroneous matches that the program returned. 56 The Michigan Court of Appeals recently decided one of the few cases related to drones, Long Lake Township v. Todd Maxon (2021), where it reversed a lower court’s decision to rule that the government would require a warrant to surveil an individual’s property with a drone. 57 In short, the judicial branch alone cannot manufacture privacy expectations—courts interpret existing law based on the Constitution, statutes, and regulations, but their interpretations depend on the judges or justices that sit on the bench, and it falls on Congress to resolve uncertainties.

In 1986, Congress enacted the Electronic Communications Privacy Act (ECPA), bundling the Wiretap Act and Stored Communications Act, to protect Americans against government privacy intrusions in their electronic communications (e.g., stored emails or live telephone conversations). However, the ECPA contains provisions that allow law enforcement to access emails and customer records without a warrant in certain contexts. 58 For example, law enforcement would require a warrant to access an unopened email that has been remotely stored for under 180 days—but after 180 days, it would be able to access that same email with only a subpoena. It can also issue a subpoena to compel companies to turn over non-content user records such as name, address, and payment information. Apart from the ECPA, Executive Order 12333 and Section 702 of the Foreign Intelligence Surveillance Act allow the federal government to gather “incidental collection” of communications content from U.S. residents who contact people located outside the United States without a warrant, contrary to Fourth Amendment protections. 59 Together, these statutes and EO grant the U.S. government broad authority to access the electronic communications of Americans, tapping into the massive troves of data that private communications companies store.

Although facial recognition meets few enacted legal restrictions at the federal level, over seven states and 20 municipalities, such as Boston, San Francisco, and Virginia, have established some limitations on government use of facial recognition usage in certain contexts. 60 For instance, Maine enacted a law in 2021 that generally prohibits government use of facial recognition except in certain cases (e.g., “serious” crimes, identification of missing or deceased individuals, and fraud prevention). 61 The same year, Minneapolis passed an ordinance to prevent the government from procuring facial recognition technology from third parties (e.g., Clearview AI) or knowingly using information collected through facial recognition, citing the technology’s higher misidentification rates for communities of color and the disproportionate burden of policing that communities of color face. 62 Yet, state and local regulations lack uniformity throughout the country, and the majority of municipalities do not have specific legal restrictions on government use of facial recognition.

(B) Protections from private companies

As we describe earlier, the private sector is integral to law enforcement operations; companies like Clearview AI often test and develop the facial recognition tools that are available to law enforcement or amass large databases that the government may have access to. Yet, in the absence of a nationwide comprehensive data privacy law, many companies face few legal limitations on how they collect, process, and transfer personal information—allowing Clearview and other companies to gather data from millions of people without clear controls to access or delete their images, and with few safeguards for security, algorithmic bias, and transparency. 63 The Federal Trade Commission (FTC) primarily investigates and enforces data protection on a national level, relying on its authority under Section 5 of the FTC Act to act against entities that engage in “unfair or deceptive acts or practices.” Using this authority, the FTC has entered consent agreements with companies like Sears (2009), Facebook (2011), Snapchat (2014), and Nomi Technologies (2015) for misrepresenting their privacy policies to their users. 64 However, this statute largely emphasizes user transparency, which has led to a system of “notice and choice,” where companies display a lengthy privacy policy and require users to consent to it before accessing their service. Notice-and-choice does not effectively preserve privacy; companies like Clearview or Amazon’s Ring can still set their own privacy policies—choosing what data they collect, store, and share, and for how long—and with the FTC’s more limited authority, the agency has only brought approximately 80 data privacy cases since 2002. 65 Privacy regulations are disjointed at the state level, and only California, Colorado, and Virginia have so far enacted comprehensive data privacy laws that give residents the rights to access and delete personal information that many companies store. In addition, five states—Arkansas, California, Illinois, Texas, and Washington—have adopted laws that regulate how private companies treat biometric information, including facial recognition. 66 Companies have treated compliance with diverging state privacy laws in two primary ways: some, like Microsoft, have pledged to voluntarily offer single-state protections (e.g., the right to access personal information) nationwide, while others, such as Clearview AI, offer different privacy settings depending on where a person lives. 67 Clearview’s website currently only allows California residents to access and delete their personal information, while Illinois residents may choose to opt out of search results. 68 Residents of the other 48 states do not experience these same privacy protections; they may submit a request for Clearview to remove search results associated with URLs that were already deleted from other websites but may not delete photos or opt out of search results for links that are still available elsewhere on the internet. Since Clearview does not advertise these controls, however, it is unclear how many individuals are aware of them or have submitted a data request.

Despite its limited privacy controls, Clearview—along with many other facial recognition companies—does not ask individuals for permission to scrape their images from public places (e.g., CCTV surveillance cameras, social media platforms, other websites). This problem is widespread; a 2020 GAO report describes a study of 30 datasets used to train facial recognition algorithms since 2006, which revealed that approximately 24 million photos had been scraped from websites without obtaining consent from the one million individuals photographed. 69

In the end, it is virtually impossible for an individual to fully opt out of facial recognition identification or control the use of their images without abstaining from public areas, the internet, or society altogether.

Since voluntary privacy protections do not apply across the entire industry—some companies offer privacy settings, while others do not—government intervention is necessary to set privacy protections for all U.S. residents, especially those communities most vulnerable to the harmful effects of surveillance.

Proposals to prevent privacy risks of facial recognition and other technologies

As both the government and private corporations feed into the problem of surveillance, gaps in current federal and state privacy laws mean that their actions to collect, use, or share data often go unchallenged. In other words, existing laws do not adequately protect user privacy among the rising ubiquity of facial recognition and other emerging technologies, fundamentally omitting the needs of communities of color that disproportionately bear the consequences of surveillance. To reduce the potential for emerging technologies to replicate historical biases in law enforcement, we summarize recent proposals that address racial bias and unequal applications of technology in the public sector. We also explain why U.S. federal privacy legislation is necessary to govern how private sector companies implement fairness in the technical development process, limit their data collection and third-party sharing, and grant more agency to the individuals they surveil.

(A) Direct measures for federal, state, and local law enforcement agencies

Although the executive branch is taking some steps to evaluate its use of artificial intelligence and equitable distribution of public services, it lacks heightened federal government-wide scrutiny over its facial recognition programs and relationships with geolocation data brokers. In October 2021, the White House announced plans to develop an AI Bill of Rights to assert basic principles of civil liberties in technology, referencing the role that facial recognition plays in discriminatory arrests as well as the privacy concerns stemming from data collection. 70 In January 2021, the Biden administration issued an executive order that directed federal agencies to conduct equity assessments to review any obstacles that marginalized communities, including individuals of color, encounter to access government services and resources. 71 These are important steps, but the role of equity assessments should be extended to appraise the appropriateness of facial recognition, access to geolocation information from data brokers, and related privacy or civil rights implications for marginalized communities for the approximately 42 federal agencies that employ law enforcement officers in some function. Short of White House guidance, federal agency review of facial recognition technologies might remain more piecemeal; for example, the Internal Revenue Service announced in early February 2022 that it would stop using the facial recognition tool ID.me for citizen verification following public outcry, but it is unclear whether other federal agencies that use the software—such as the United States Patent and Trademark Office and Social Security Administration—will choose to do so as well. 72 Federal law enforcement reform could also occur through an act of Congress, and legislators have introduced several bills that also propose new guardrails for executive agencies that conduct surveillance. In March 2021, the House of Representatives passed the George Floyd Justice in Policing Act which, among other provisions, would prohibit federal law enforcement officers from deploying facial recognition in their body cameras or patrol vehicle cameras. 73 The Facial Recognition and Biometric Technology Moratorium Act, which Sen. Ed Markey (D-Mass.) and Rep. Pramila Jayapal (D-Wash.) introduced in June 2021, aims to ban the federal government’s use of biometric surveillance systems unless otherwise authorized by law. 74 The Facial Recognition Technology Warrant Act, which Sens. Chris Coons (D-Del.) and Mike Lee (R-Utah) proposed in 2019 during the previous Congress, included a warrant requirement for federal law enforcement officers to conduct “ongoing” surveillance of an individual in public areas with facial recognition for over 72 hours. 75 In April 2021, Rep. Jerrold Nadler (D-N.Y.) and Sen. Ron Wyden (D-Ore.) introduced The Fourth Amendment Is Not For Sale Act to mitigate federal law enforcement’s access to information from “electronic communication services” or “remote computing services” in a way that violates privacy policy agreements or is otherwise deceptive, primarily targeting concerns over the government’s purchase of geolocation information from data brokers like Venntel or X-Mode without a warrant. 76 These proposed bills outline some of the existing problems with surveillance oversight: a lack of guardrails and transparency to prevent law enforcement’s abuse of facial recognition and access to geolocation and communications data. Yet, they are not complete fixes. If enacted into law, the Fourth Amendment Is Not For Sale Act could prevent any attempts by law enforcement agencies to bypass due process or a probable cause warrant by purchasing communications or location data from private companies—but such a moratorium would be largely conditional on a website’s terms of service or privacy policies. 77 Similarly, the George Floyd Justice in Policing Act, Facial Recognition Technology Warrant Act, and Facial Recognition Biometric Technology Moratorium Act could address federal law enforcement agencies’ use of facial recognition, but would not affect state and local police officers’ use of the technology. 78

Because state and local governments have jurisdiction over policing in their areas, Congress and the federal executive branch have limited means to improve policing practices everywhere in the United States. 79 Still, as privacy concerns over facial recognition and surveillance grow, more state and local governments and police departments can individually consider measures to specify the contexts in which it is appropriate to use facial recognition and the necessary processes to do so (e.g., with a probable cause warrant). 80 In 2016, Georgetown Law researchers Clare Garvie, Alvaro Bedoya, and Jonathan Frankle proposed one possible framework for “acceptable uses of facial recognition” for law enforcement; for example, an individual with special training in facial recognition would be permitted to use the software to identify somebody on surveillance camera footage if officers have a “reasonable suspicion” that they committed a felony. 81 In addition to how to use the technology, such training would promote awareness of the “limitations of facial recognition” and the “appropriateness [of images] for face recognition searches.” 82 Ideally, this should also include an educational foundation in racial bias and ethics of surveillance for law enforcement officers at the federal, state, and local levels. Brookings researcher Rashawn Ray has also supported training opportunities for state and local law enforcement as part of a holistic approach to increase accountability around racial profiling. Ray recently testified on this issue before the Virginia Advisory Committee to the U.S. Commission on Civil Rights, describing how police departments can host implicit bias and mental health trainings for officers, invite community members to sit on police oversight or misconduct trial boards, and provide housing stipends to help officers reside in their local communities. 83 Georgetown Law professor Laura Moy has also put forward a comprehensive list of questions that police departments might use to assess their use of surveillance technology, modeled after the racial equity impact assessments used by the Minneapolis Board of Education and others. 84 The proposals by Garvie, Bedoya, Frankle, Ray, and Moy are a valuable starting point for federal, state, and local law enforcement agencies to consider in application—and moreover, they demonstrate a need for police departments to actively work with civil society, academic researchers, and advocacy groups to provide input on prioritizing racial equity in police technology.

(B) The role of federal privacy legislation

Although Congress does not oversee state and local police departments, there is one clear-cut action it could take that would have an indirect—yet significant—impact on government surveillance across the nation: to pass a comprehensive federal privacy law that regulates the data practices of private companies. Government agencies often purchase or license facial recognition software from private companies, and businesses can either voluntarily share or be legally compelled to disclose large amounts of personal information to law enforcement. 85 Despite the general lack of comprehensive privacy regulations in the United States, the U.S. private sector provides unprecedented resources that immensely enhance the surveillance capabilities of law enforcement agencies. 86 Should Congress pass a federal privacy law to govern how private companies collect and use data, the effects would not only increase privacy protections for all Americans but reduce the possibility of surveillance abuse against communities of color in the law enforcement context. First, Congress could introduce a requirement for businesses to allow individuals to access and delete personal information that they hold—allowing anybody to become aware of and erase their images in facial recognition databases like Clearview, and meaningfully increasing the transparency of data collection. 87 Next, Congress could enshrine common sense limitations in data collection, storage, and retention for private companies into law—this, in turn, would limit the amount of data that law enforcement agencies could access either voluntarily or through subpoenas or warrants. It should establish baseline principles like data minimization—only allowing private companies to collect, use, and share data in ways that are necessary to the original business purpose—to reduce extraneous data collection and potential for surveillance. These principles are not inconceivable in practice: residents of California, Virginia, Colorado, and the European Union already possess similar protections, and pending legislation such as Sen. Maria Cantwell’s (D-Wash.) Consumer Online Privacy Rights Act and Sen. Roger Wicker’s (R-Miss.) SAFE DATA Act have been introduced to accord these provisions to all Americans. 88

But Congress needs to go further than general privacy provisions and embody additional measures to address facial recognition and biometric information, given their outsized potential to result in disparate impact in the law enforcement context. Federal privacy legislation could also advance this objective; Congress could direct the Federal Trade Commission to study the impact of biometric information, including algorithmic outcomes, on civil rights in highly sensitive scenarios such as law enforcement. Current federal privacy bills or proposals take different approaches to biometric information—some, such as Sen. Sherrod Brown’s (D-Ohio) draft Data Accountability and Transparency Act of 2021, would ban “data aggregators” from using facial recognition technology altogether, while on the other end of the spectrum, Wicker’s SAFE DATA Act would simply require companies to obtain consent from individuals before processing or sharing biometric information with third parties. 89 Likely, some solution would be necessary in the middle: clear guardrails on how private companies collect, process, and transfer biometric information in a manner that would allow them to use and improve the technology in appropriate contexts while also preventing misuse. Congress could direct the FTC to create these regulations, based on the findings of their study and input from civil society.

Legislation can require businesses that use personal information to develop or deploy algorithms to audit both their products and outcomes to prevent disparate impact. A number of researchers, such as Dillon Reisman, Jason Schultz, Kate Crawford, and Meredith Whittaker of New York University’s AI Now Institute have conceptualized “algorithmic impact assessments” to help government agencies or companies to evaluate the accuracy, potential community harms or benefits, and risk of bias or discrimination before deploying automated tools. 90 Bills like the Algorithmic Accountability Act, which Rep. Yvette Clarke (D-N.Y.) and Sen. Ron Wyden (D-Ore.) reintroduced in February 2022, would also require companies that deploy AI for critical decisions to document the representativeness of their input datasets, sources of data collection, any alternatives or considerations to the input data, and overall methodology. 91 In any framework to evaluate the use of facial recognition or other surveillance tools, impact assessments will be critical to help users and developers audit algorithms for accuracy and racial equity both in development and in the context of application. More importantly, the private sector cannot be the sole arbiter of truth when it comes to the performance of these systems; law enforcement must evaluate products and services to anticipate potential privacy risks and actively examine the inclusivity of datasets and potential risks of replicating patterns of marginalization.

From this review, it is clear that facial recognition and surveillance technologies have shifted the balance of power toward law enforcement agencies. That is why privacy protections are more important than ever for all Americans—and they are especially so for the communities of color that may suffer the greatest consequences from their absence.

The authors would like to thank Samantha Lai for editing assistance, Emily Skahill for research support, and Cameron Kerry and Darrell West for feedback and comments.

The Brookings Institution is a nonprofit organization devoted to independent research and policy solutions. Its mission is to conduct high-quality, independent research and, based on that research, to provide innovative, practical recommendations for policymakers and the public. The conclusions and recommendations of any Brookings publication are solely those of its author(s), and do not reflect the views of the Institution, its management, or its other scholars.

Amazon, Apple, Facebook, Google, IBM, and Microsoft provide general, unrestricted support to the Institution. The findings, interpretations, and conclusions in this report are not influenced by any donation. Brookings recognizes that the value it provides is in its absolute commitment to quality, independence, and impact. Activities supported by its donors reflect this commitment.

Related Books

Gloria González Fuster, Valsamis Mitsilegas, Elspeth Guild, Sergio Carrera

July 19, 2016

Jacob Parakilas, Hannah Bryce, Kenneth Cukier, Missy Cummings

August 28, 2018

“Federal Bureau of Investigation (FBI),” Stanford University, The Martin Luther King, Jr. Research and Education Institute, accessed February 24, 2022, https://kinginstitute.stanford.edu/encyclopedia/federal-bureau-investigation-fbi ; Alvaro M. Bedoya, “What the FBI’s Surveillance of Martin Luther King Tells Us About the Modern Spy Era,” Slate Magazine, January 18, 2016, https://slate.com/technology/2016/01/what-the-fbis-surveillance-of-martin-luther-king-says-about-modern-spying.html ; Virgie Hoban, “‘Discredit, Disrupt, and Destroy’: FBI Records Acquired by the Library Reveal Violent Surveillance of Black Leaders, Civil Rights Organizations,” University of California, Berkeley Library News, accessed February 24, 2022, https://news.lib.berkeley.edu/fbi ; Sam Briger, “Documentary Exposes How The FBI Tried To Destroy MLK With Wiretaps, Blackmail,” NPR, January 18, 2021, https://www.npr.org/2021/01/18/956741992/documentary-exposes-how-the-fbi-tried-to-destroy-mlk-with-wiretaps-blackmail . ( Back to top )
George Joseph, “Exclusive: Feds Regularly Monitored Black Lives Matter Since Ferguson,” The Intercept, July 24, 2015, https://theintercept.com/2015/07/24/documents-show-department-homeland-security-monitoring-black-lives-matter-since-ferguson/ . ( Back to top )
Kevin Rector and Alison Knezevich, “Maryland’s Use of Facial Recognition Software Questioned by Researchers, Civil Liberties Advocates,” The Baltimore Sun, October 18, 2016, https://www.baltimoresun.com/news/crime/bs-md-facial-recognition-20161017-story.html ; Shira Ovide, “A Case for Banning Facial Recognition,” The New York Times, June 9, 2020, https://www.nytimes.com/2020/06/09/technology/facial-recognition-software.html . ( Back to top )
Zolan Kanno-Youngs, “U.S. Watched George Floyd Protests in 15 Cities Using Aerial Surveillance,” The New York Times, June 19, 2020, https://www.nytimes.com/2020/06/19/us/politics/george-floyd-protests-surveillance.html . ( Back to top )
“Information About the Department of Justice’s China Initiative and a Compilation of China-Related Prosecutions Since 2018,” U.S. Department of Justice, July 31, 2020, https://www.justice.gov/archives/nsd/information-about-department-justice-s-china-initiative-and-compilation-china-related ; Ryan Lucas, “The Justice Department is ending its controversial China Initiative,” NPR, February 23, 2022, https://www.npr.org/2022/02/23/1082593735/justice-department-china-initiative . ( Back to top )
Michael German and Alex Liang, “Why Ending the Justice Department’s ‘China Initiative’ Is Vital to U.S. Security,” Just Security, January 3, 2022, https://www.justsecurity.org/79698/why-ending-the-justice-departments-china-initiative-is-vital-to-u-s-security/ ; Matt Apuzzo, “U.S. Drops Charges That Professor Shared Technology With China,” The New York Times, September 11, 2015, https://www.nytimes.com/2015/09/12/us/politics/us-drops-charges-that-professor-shared-technology-with-china.html ; Don Lee, “Why Trump’s Anti-Spy ‘China Initiative’ Is Unraveling,” Los Angeles Times, September 16, 2021, https://www.latimes.com/politics/story/2021-09-16/why-trump-china-initiative-unraveling ; Emma Coffey, “University Offers to Reinstate Professor Acquitted of Espionage Charges,” University of Texas, The Daily Beacon, October 29, 2021, https://www.utdailybeacon.com/campus_news/academics/university-offers-to-reinstate-professor-acquitted-of-espionage-charges/article_f6d0aabe-38ee-11ec-9c23-57a37bddf43c.html ; Nicole Perlroth, “Accused of Spying for China, Until She Wasn’t,” The New York Times, May 9, 2015, https://www.nytimes.com/2015/05/10/business/accused-of-spying-for-china-until-she-wasnt.html . ( Back to top )
Nina Wallace, “Of Spies and G-Men: How the U.S. Government Turned Japanese Americans into Enemies of the State,” Densho: Japanese American Incarceration and Japanese Internment, September 29, 2017, https://densho.org/catalyst/of-spies-and-gmen/ ; Pedro A. Loureiro, “Japanese Espionage and American Countermeasures in Pre—Pearl Harbor California,” The Journal of American-East Asian Relations 3, no. 3 (1994): 197–210, https://www.jstor.org/stable/23612532 ; “Statement – The Japanese American Citizens League,” American Civil Liberties Union, accessed February 24, 2022, https://www.aclu.org/other/statement-japanese-american-citizens-league ; Lori Aratani, “Secret Use of Census Info Helped Send Japanese Americans to Internment Camps in WWII,” The Washington Post, April 3, 2018, https://www.washingtonpost.com/news/retropolis/wp/2018/04/03/secret-use-of-census-info-helped-send-japanese-americans-to-internment-camps-in-wwii/ . ( Back to top )
Alvaro M. Bedoya, “What the FBI’s Surveillance of Martin Luther King Tells Us About the Modern Spy Era,” Slate Magazine, January 18, 2016, https://slate.com/technology/2016/01/what-the-fbis-surveillance-of-martin-luther-king-says-about-modern-spying.html ; Adam Goldman and Matt Apuzzo, “NYPD Muslim Spying Led to No Leads, Terror Cases,” The Associated Press, August 21, 2012, https://www.ap.org/ap-in-the-news/2012/nypd-muslim-spying-led-to-no-leads-terror-cases ; Adam Goldman and Matt Apuzzo, “With Cameras, Informants, NYPD Eyed Mosques,” The Associated Press, February 23, 2012, https://www.ap.org/ap-in-the-news/2012/with-cameras-informants-nypd-eyed-mosques . ( Back to top )
“U.S. Muslims Concerned About Their Place in Society, but Continue to Believe in the American Dream,” Pew Research Center, Religion & Public Life Project, July 26, 2017, https://www.pewforum.org/2017/07/26/findings-from-pew-research-centers-2017-survey-of-us-muslims/ . ( Back to top )
Tatiana Walk-Morris, “What to Do If You Face Anti-Muslim Discrimination at Airport Security,” Vice, September 10, 2021, https://www.vice.com/en/article/epnwjz/what-to-do-if-you-face-anti-muslim-discrimination-islamophobia-at-airport-security . ( Back to top )
Adam Goldman and Matt Apuzzo, “NYPD Muslim Spying Led to No Leads, Terror Cases,” The Associated Press, August 21, 2012, https://www.ap.org/ap-in-the-news/2012/nypd-muslim-spying-led-to-no-leads-terror-cases ; Mike Ahlers and Jeanne Meserve, “Muslim-American Group Criticizes TSA Plan as Profiling,” CNN, January 4, 2010, http://www.cnn.com/2010/CRIME/01/04/tsa.measures.muslims/index.html . ( Back to top )
John Davis, “Walls Work,” U.S. Customs and Border Protection, accessed February 24, 2022, https://www.cbp.gov/frontline/border-security ; McKenzie Funk, “How ICE Picks Its Targets in the Surveillance Age,” The New York Times, October 2, 2019, https://www.nytimes.com/2019/10/02/magazine/ice-surveillance-deportation.html ; Emma Li, “Mass and Intrusive Surveillance of Immigrants Is an Unacceptable Alternative to Detention,” Center for Democracy and Technology (blog), August 5, 2021, https://cdt.org/insights/mass-and-intrusive-surveillance-of-immigrants-is-an-unacceptable-alternative-to-detention/ . ( Back to top )
Brad Heath, “U.S. Secretly Tracked Billions of Calls for Decades,” USA TODAY, April 7, 2015, https://www.usatoday.com/story/news/2015/04/07/dea-bulk-telephone-surveillance-operation/70808616/ ; Alvaro M. Bedoya, “What the FBI’s Surveillance of Martin Luther King Tells Us About the Modern Spy Era,” Slate Magazine, January 18, 2016, https://slate.com/technology/2016/01/what-the-fbis-surveillance-of-martin-luther-king-says-about-modern-spying.html . ( Back to top )
Andrew Guthrie Ferguson, “Facial Recognition and the Fourth Amendment,” Minnesota Law Review 3204 (2021), https://scholarship.law.umn.edu/mlr/3204 . ( Back to top )
Katelyn Ringrose, “Law Enforcement’s Pairing of Facial Recognition Technology with Body-Worn Cameras Escalates Privacy Concerns,” Virginia Law Review Online 105 (2019): 57, https://www.virginialawreview.org/articles/law-enforcements-pairing-facial-recognition-technology-body-worn-cameras-escalates/ . ( Back to top )
“Facial Recognition Technology: Federal Law Enforcement Agencies Should Have Better Awareness of Systems Used By Employees,” U.S. Government Accountability Office, July 13, 2021, https://www.gao.gov/products/gao-21-105309 ; Clare Garvie, Alvaro Bedoya, and Jonathan Frankle, “The Perpetual Line-Up: Unregulated Police Face Recognition in America,” Georgetown Law, Center on Privacy & Technology, October 18, 2016, https://www.perpetuallineup.org/ . ( Back to top)
Kashmir Hill, “The Secretive Company That Might End Privacy as We Know It,” The New York Times, January 18, 2020, https://www.nytimes.com/2020/01/18/technology/clearview-privacy-facial-recognition.html . ( Back to top)
Eli Watkins, “Watchdog Says FBI Has Access to More than 641 Million ‘Face Photos’,” CNN, June 4, 2019, https://www.cnn.com/2019/06/04/politics/gao-fbi-face-photos/index.html ; Will Knight, “Clearview AI Has New Tools to Identify You in Photos,” Wired, October 4, 2021, https://www.wired.com/story/clearview-ai-new-tools-identify-you-photos/ . ( Back to top )
Max Rivlin-Nadler, “How ICE Uses Social Media to Surveil and Arrest Immigrants,” The Intercept, December 22, 2019, https://theintercept.com/2019/12/22/ice-social-media-surveillance/ . ( Back to top )
Conor Friedersdorf, “An Unprecedented Threat to Privacy,” The Atlantic, January 27, 2016, https://www.theatlantic.com/politics/archive/2016/01/vigilant-solutions-surveillance/427047/ . ( Back to top)
”Facial Recognition Technology: Current and Planned Uses by Federal Agencies,” U.S. Government Accountability Office, August 24, 2021, https://www.gao.gov/products/gao-21-526 ; “Vigilant FaceSearch – Facial Recognition System,” Motorola Solutions, accessed February 24, 2022, https://www.motorolasolutions.com/en_us/products/command-center-software/analysis-and-investigation/vigilant-facesearch-facial-recognition-system.html . ( Back to top)
Joseph Cox, “Tech Firm Offers Cops Facial Recognition to ID Homeless People,” Vice, February 8, 2022, https://www.vice.com/en/article/wxdp7x/tech-firm-facial-recognition-homeless-people-odin . ( Back to top)
Jeffrey Dastin, “Amazon Extends Moratorium on Police Use of Facial Recognition Software,” Reuters, May 18, 2021, https://www.reuters.com/technology/exclusive-amazon-extends-moratorium-police-use-facial-recognition-software-2021-05-18/ . ( Back to top)
Sara Morrison, “Here’s How Police Can Get Your Data — Even If You Aren’t Suspected of a Crime,” Vox, July 31, 2021, https://www.vox.com/recode/22565926/police-law-enforcement-data-warrant . ( Back to top)
Matt O’Brien and Michael Liedtke, “How Big Tech Created a Data ‘treasure Trove’ for Police,” AP News, June 22, 2021, https://apnews.com/article/how-big-tech-created-data-treasure-trove-for-police-e8a664c7814cc6dd560ba0e0c435bf90 . ( Back to top)
Sara Morrison, “A Surprising Number of Government Agencies Buy Cellphone Location Data. Lawmakers Want to Know Why,” Vox, December 2, 2020, https://www.vox.com/recode/22038383/dhs-cbp-investigation-cellphone-data-brokers-venntel ; Joseph Cox, “How the U.S. Military Buys Location Data from Ordinary Apps,” Vice, November 16, 2020, https://www.vice.com/en/article/jgqm5x/us-military-location-data-xmode-locate-x . ( Back to top)
Jon Keegan and Alfred Ng, “There’s a Multibillion-Dollar Market for Your Phone’s Location Data,” The Markup, September 30, 2021, https://themarkup.org/privacy/2021/09/30/theres-a-multibillion-dollar-market-for-your-phones-location-data ; Byron Tau and Michelle Hackman, “Federal Agencies Use Cellphone Location Data for Immigrant Enforcement,” The Wall Street Journal, February 7, 2020, https://www.wsj.com/articles/federal-agencies-use-cellphone-location-data-for-immigration-enforcement-11581078600 . ( Back to top)
Max Rivlin-Nadler, “How ICE uses social media to surveil and arrest immigrants,” The Intercept, December 22, 2019, https://theintercept.com/2019/12/22/ice-social-media-surveillance/ ; “Social media surveillance by Homeland Security Investigations: A threat to immigrant communities and free expression,” Brennan Center for Justice, November 15, 2019, https://www.brennancenter.org/our-work/research-reports/social-media-surveillance-homeland-security-investigations-threat . ( Back to top)
Max Rivlin-Nadler, “How ICE Uses Social Media to Surveil and Arrest Immigrants,” The Intercept, December 22, 2019, https://theintercept.com/2019/12/22/ice-social-media-surveillance/ ; Mary Pat Dwyer and José Guillermo Gutiérrez, “Documents Reveal LAPD Collected Millions of Tweets from Users Nationwide,” Brennan Center for Justice, December 15, 2021, https://www.brennancenter.org/our-work/analysis-opinion/documents-reveal-lapd-collected-millions-tweets-users-nationwide . ( Back to top)
Matthew Guariglia, “How Are Police Using Drones?” Electronic Frontier Foundation, January 6, 2022, https://www.eff.org/deeplinks/2022/01/how-are-police-using-drones . ( Back to top)
Faine Greenwood, “The Chula Vista, California, Police Department’s One-of-a-Kind Drone Program,” Slate Magazine, May 17, 2021, https://slate.com/technology/2021/05/chula-vista-police-drone-program.html . ( Back to top) /li>
Dawn Kawamoto, “Cops Wearing Cameras: What Happens When Privacy and Accountability Collide?” GovTech, accessed February 24, 2022, https://www.govtech.com/biz/Cops-Wearing-Cameras-What-Happens-When-Privacy-and-Accountability-Collide.html ; Bryce C. Newell, “Body Cameras Help Monitor Police but Can Invade People’s Privacy,” The Conversation, May 25, 2021, http://theconversation.com/body-cameras-help-monitor-police-but-can-invade-peoples-privacy-160846 ; Jennifer Lee, “Will Body Cameras Help End Police Violence?” ACLU of Washington, June 7, 2021, https://www.aclu-wa.org/story/%C2%A0will-body-cameras-help-end-police-violence%C2%A0 ; German Lopez, “The Failure of Policy Body Cameras,” Vox, July 21, 2017, https://www.vox.com/policy-and-politics/2017/7/21/15983842/police-body-cameras-failures . ( Back to top)
Rani Molla, “The Rise of Fear-Based Social Media like Nextdoor, Citizen, and Now Amazon’s Neighbors,” Vox, May 7, 2019, https://www.vox.com/recode/2019/5/7/18528014/fear-social-media-nextdoor-citizen-amazon-ring-neighbors ; Jessi Hempel, “For Nextdoor, Eliminating Racism Is No Quick Fix,” Wired, February 16, 2017, https://www.wired.com/2017/02/for-nextdoor-eliminating-racism-is-no-quick-fix/ . ( Back to top)
Rani Molla, “Amazon Ring Sales Nearly Tripled in December despite Hacks,” Vox, January 21, 2020, https://www.vox.com/recode/2020/1/21/21070402/amazon-ring-sales-jumpshot-data ; Thorin Klosowski, “Facial Recognition Is Everywhere. Here’s What We Can Do About It,” The New York Times Wirecutter (blog), July 15, 2020, https://www.nytimes.com/wirecutter/blog/how-facial-recognition-works/ . ( Back to top)
Lauren Bridges, “Amazon’s Ring Is the Largest Civilian Surveillance Network the US Has Ever Seen,” The Guardian, May 18, 2021, http://www.theguardian.com/commentisfree/2021/may/18/amazon-ring-largest-civilian-surveillance-network-us ; Rani Molla, “How Amazon’s Ring Is Creating a Surveillance Network with Video Doorbells,” Vox, September 5, 2019, https://www.vox.com/2019/9/5/20849846/amazon-ring-explainer-video-doorbell-hacks . ( Back to top)
Kashmir Hill, “Another Arrest, and Jail Time, Due to a Bad Facial Recognition Match,” The New York Times, December 29, 2020, https://www.nytimes.com/2020/12/29/technology/facial-recognition-misidentify-jail.html . ( Back to top)
Joy Buolamwini and Timnit Gebru, “Gender Shades: Intersectional Accuracy Disparities in Commercial Gender Classification,” Conference on fairness, accountability and transparency: PMLR, 2018, https://proceedings.mlr.press/v81/buolamwini18a/buolamwini18a.pdf . ( Back to top)
“NIST Study Evaluates Effects of Race, Age, Sex on Face Recognition Software,” U.S. National Institute of Standards and Technology, December 19, 2019, https://www.nist.gov/news-events/news/2019/12/nist-study-evaluates-effects-race-age-sex-face-recognition-software ; Natasha Singer and Cade Metz, “Many Facial-Recognition Systems Are Biased, Says U.S. Study,” The New York Times, December 19, 2019, https://www.nytimes.com/2019/12/19/technology/facial-recognition-bias.html ; Drew Harwell, “Federal Study Confirms Racial Bias of Many Facial-Recognition Systems, Casts Doubt on Their Expanding Use,” The Washington Post, December 19, 2019, https://www.washingtonpost.com/technology/2019/12/19/federal-study-confirms-racial-bias-many-facial-recognition-systems-casts-doubt-their-expanding-use/ . ( Back to top)
“Amazon Rekognition Improves Accuracy of Real-Time Face Recognition and Verification,” Amazon Web Services, April 2, 2018, https://aws.amazon.com/about-aws/whats-new/2018/04/amazon-rekognition-improves-accuracy-of-real-time-face-recognition-and-verification/ ; Brad Smith, “Facial Recognition: It’s Time for Action,” Microsoft On the Issues, December 6, 2018, https://blogs.microsoft.com/on-the-issues/2018/12/06/facial-recognition-its-time-for-action/ . ( Back to top)
Jon Porter, “Federal Study of Top Facial Recognition Algorithms Finds ‘Empirical Evidence’ of Bias,” The Verge, December 20, 2019, https://www.theverge.com/2019/12/20/21031255/facial-recognition-algorithm-bias-gender-race-age-federal-nest-investigation-analysis-amazon . ( Back to top)
Jennifer Lynch, “Face Off: Law Enforcement Use of Face Recognition Technology,” Electronic Frontier Foundation, February 12, 2018, https://www.eff.org/wp/law-enforcement-use-face-recognition . ( Back to top)
“Criminal Justice Fact Sheet,” NAACP, May 24, 2021, https://naacp.org/resources/criminal-justice-fact-sheet . ( Back to top)
Laura Moy, “A Taxonomy of Police Technology’s Racial Inequity Problems,” U. Ill. L. Rev. 139 (2021), http://dx.doi.org/10.2139/ssrn.3340898 . ( Back to top)
Motion for a European Parliament resolution on artificial intelligence in criminal law and its use by the police and judicial authorities in criminal matters, 2020/2016(INI), European Parliament (adopted 2021), https://www.europarl.europa.eu/doceo/document/A-9-2021-0232_EN.html ?. ( Back to top)
The AI Act, COM/2021/206, European Commission (2021), https://eur-lex.europa.eu/legal-content/EN/TXT/HTML/?uri=CELEX:52021PC0206&from=EN . ( Back to top)
Anita L. Allen, “Dismantling the ‘Black Opticon’: Privacy, Race, Equity, and Online Data-Protection Reform,” The Yale Law Journal 131, November 16, 2021, https://www.yalelawjournal.org/forum/dismantling-the-black-opticon . ( Back to top)
Samuel D. Warren and Louis D. Brandeis, “Right to privacy,” Harv. L. Rev. 4 (1890): 193, https://www.cs.cornell.edu/~shmat/courses/cs5436/warren-brandeis.pdf . ( Back to top)
Nicandro Iannacci, “Recalling the Supreme Court’s Historic Statement on Contraception and Privacy,” National Constitution Center, June 7, 2019, https://constitutioncenter.org/blog/contraception-marriage-and-the-right-to-privacy . ( Back to top)
Elizabeth Goitein, “The government can’t seize your digital data. Except by buying it,” The Washington Post, April 26, 2021, https://www.washingtonpost.com/outlook/2021/04/26/constitution-digital-privacy-loopholes-purchases/ . ( Back to top)
Caitlin Chin, “Highlights: Setting Guidelines for Facial Recognition and Law Enforcement,” The Brookings Institution (blog), December 9, 2019, https://www.brookings.edu/blog/techtank/2019/12/09/highlights-setting-guidelines-for-facial-recognition-and-law-enforcement/ . ( Back to top)
Riley v. California, 573 U.S. 373 (2014). https://www.supremecourt.gov/opinions/13pdf/13-132_8l9c.pdf . ( Back to top)
Carpenter v. United States, 585 U.S. __ (2018). https://www.supremecourt.gov/opinions/17pdf/16-402_h315.pdf . ( Back to top)
Florida v. Riley, 488 U.S. 445 (1989). https://supreme.justia.com/cases/federal/us/488/445/ . ( Back to top)
Rebecca Darin Goldberg, “You Can See My Face, Why Can’t I? Facial Recognition and Brady,” Columbia Human Rights Law Review, April 12, 2021, http://hrlr.law.columbia.edu/hrlr-online/you-can-see-my-face-why-cant-i-facial-recognition-and-brady/ . ( Back to top)
Willie Allen Lynch v. State of Florida (2018). https://cases.justia.com/florida/first-district-court-of-appeal/2018-16-3290.pdf?ts=1545938765 ; Aaron Mak, “Facing Facts,” Slate, January 25, 2019, https://slate.com/technology/2019/01/facial-recognition-arrest-transparency-willie-allen-lynch.html . ( Back to top)
Long Lake Township v. Todd Maxon and Heather Maxon (2021). https://www.courts.michigan.gov/siteassets/case-documents/uploads/OPINIONS/FINAL/COA/20210318_C349230_47_349230.OPN.PDF ; Matthew Feeney, “Does the 4th Amendment Prohibit Warrantless Drone Surveillance?” Cato Institute, March 24, 2021, https://www.cato.org/blog/does-4th-amendment-prohibit-warrantless-drone-surveillance . ( Back to top)
“Electronic Communications Privacy Act (ECPA),” Electronic Privacy Information Center, accessed February 24, 2022, https://epic.org/ecpa/ . ( Back to top)
Elizabeth Goitein, “How the CIA Is Acting Outside the Law to Spy on Americans,” Brennan Center for Justice, February 15, 2022, https://www.brennancenter.org/our-work/analysis-opinion/how-cia-acting-outside-law-spy-americans ; “‘Incidental,’ Not Accidental, Collection,” Electronic Frontier Foundation, October 2, 2017, https://www.eff.org/pages/Incidental-collection . ( Back to top)
“States Push Back Against Use of Facial Recognition by Police,” US News, May 5, 2021, https://www.usnews.com/news/politics/articles/2021-05-05/states-push-back-against-use-of-facial-recognition-by-police ; “General FR / Surveillance Regulation,” NYU School of Law, Policing Project, accessed September 24, 2022, https://www.policingproject.org/general-regulations . ( Back to top)
“Maine Enacts Strongest Statewide Facial Recognition Regulations in the Country,” American Civil Liberties Union, June 30, 2021, https://www.aclu.org/press-releases/maine-enacts-strongest-statewide-facial-recognition-regulations-country . ( Back to top)
Kim Lyons, “Minneapolis Prohibits Use of Facial Recognition Software by Its Police Department,” The Verge, February 13, 2021, https://www.theverge.com/2021/2/13/22281523/minneapolis-prohibits-facial-recognition-software-police-privacy . ( Back to top)
Cameron F. Kerry, “Why Protecting Privacy Is a Losing Game Today—and How to Change the Game,” The Brookings Institution (blog), July 12, 2018, https://www.brookings.edu/research/why-protecting-privacy-is-a-losing-game-today-and-how-to-change-the-game/ . ( Back to top)
“Sears Settles FTC Charges Regarding Tracking Software,” Federal Trade Commission, June 4, 2009, https://www.ftc.gov/news-events/press-releases/2009/06/sears-settles-ftc-charges-regarding-tracking-software ; “Facebook Settles FTC Charges That It Deceived Consumers By Failing To Keep Privacy Promises,” Federal Trade Commission, November 29, 2011, https://www.ftc.gov/news-events/press-releases/2011/11/facebook-settles-ftc-charges-it-deceived-consumers-failing-keep ; “FTC Approves Final Order Settling Charges Against Snapchat,” Federal Trade Commission, December 31, 2014, https://www.ftc.gov/news-events/press-releases/2014/12/ftc-approves-final-order-settling-charges-against-snapchat ; “Retail Tracking Firm Settles FTC Charges It Misled Consumers About Opt Out Choices,” Federal Trade Commission, April 23, 2015, https://www.ftc.gov/news-events/press-releases/2015/04/retail-tracking-firm-settles-ftc-charges-it-misled-consumers . ( Back to top)
Cameron F. Kerry and Caitlin Chin, “Hitting Refresh on Privacy Policies: Recommendations for Notice and Transparency,” The Brookings Institution (blog), January 6, 2020, https://www.brookings.edu/blog/techtank/2020/01/06/hitting-refresh-on-privacy-policies-recommendations-for-notice-and-transparency/ ; “Federal Trade Commission 2020 Privacy and Data Security Update,” Federal Trade Commission, 2020, https://www.ftc.gov/system/files/documents/reports/federal-trade-commission-2020-privacy-data-security-update/20210524_privacy_and_data_security_annual_update.pdf . ( Back to top )
Christopher Ward and Kelsey C. Boehm, “Developments in Biometric Information Privacy Laws,” Foley & Lardner LLP (blog), June 17, 2021, https://www.foley.com/en/insights/publications/2021/06/developments-biometric-information-privacy-laws . ( Back to top )
Julie Brill, “Microsoft Will Honor California’s New Privacy Rights throughout the United States,” Microsoft On the Issues (blog), November 11, 2019, https://blogs.microsoft.com/on-the-issues/2019/11/11/microsoft-california-privacy-rights/ . ( Back to top )
“Privacy & Requests,” Clearview AI, accessed February 24, 2022, https://www.clearview.ai/privacy-and-requests . ( Back to top )
“Facial Recognition Technology: Privacy and Accuracy Issues Related to Commercial Uses, U.S. Government Accountability Office, July 13, 2020, https://www.gao.gov/products/gao-20-522 . ( Back to top )
Eric Lander and Alondra Nelson, “ICYMI: WIRED (Opinion): Americans Need a Bill of Rights for an AI-Powered World,” The White House Office of Science and Technology (blog), October 22, 2021, https://www.whitehouse.gov/ostp/news-updates/2021/10/22/icymi-wired-opinion-americans-need-a-bill-of-rights-for-an-ai-powered-world/ . ( Back to top )
“Executive Order On Advancing Racial Equity and Support for Underserved Communities Through the Federal Government,” The White House, January 20, 2021, https://www.whitehouse.gov/briefing-room/presidential-actions/2021/01/20/executive-order-advancing-racial-equity-and-support-for-underserved-communities-through-the-federal-government/ . ( Back to top )
“IRS announces transition away from use of third-party verification involving facial recognition,” Internal Revenue Service, February 7, 2022, https://www.irs.gov/newsroom/irs-announces-transition-away-from-use-of-third-party-verification-involving-facial-recognition ; Alan Rappeport, “I.R.S. Will Allow Taxpayers to Forgo Facial Recognition Amid Blowback,” The New York Times, February 21, 2022, https://www.nytimes.com/2022/02/21/us/politics/irs-facial-recognition.html ; Rachel Metz, “IRS Halts Plans to Require Facial Recognition For Logging In To User Accounts,” CNN Business, February 7, 2022, https://www.cnn.com/2022/02/07/tech/irs-facial-recognition-idme/index.html . ( Back to top )
George Floyd Justice in Policing Act of 2021, H.R. 1280, 117th Congress (2021-2022), https://www.congress.gov/bill/117th-congress/house-bill/1280/text . ( Back to top )
Facial Recognition and Biometric Technology Moratorium Act of 2021, S. 2052, 117th Congress (2021-2022), https://www.congress.gov/bill/117th-congress/senate-bill/2052/text . ( Back to top )
Facial Recognition Technology Warrant Act of 2019, S. 2878, 116th Congress (2019-2020), https://www.congress.gov/bill/116th-congress/senate-bill/2878/text . ( Back to top )
Fourth Amendment Is Not For Sale Act, S. 1265, 117th Congress (2021-2022), https://www.congress.gov/bill/117th-congress/senate-bill/1265/text . ( Back to top )
Sara Morrison, “Here’s How Police Can Get Your Data — Even If You Aren’t Suspected of a Crime,” Vox, July 31, 2021, https://www.vox.com/recode/22565926/police-law-enforcement-data-warrant . ( Back to top )
Daniel E. Bromberg and Étienne Charbonneau, “Americans Want Police to Release Body-Cam Footage. But There’s a Bigger Worry,” The Washington Post, May 5, 2021, https://www.washingtonpost.com/politics/2021/05/05/americans-want-police-release-bodycam-footage-theres-bigger-worry/ . ( Back to top )
“State and Local Government,” The White House, accessed February 24, 2022, https://www.whitehouse.gov/about-the-white-house/our-government/state-local-government/ ; Alexis Karteron, “Congress Can’t Do Much about Fixing Local Police – but It Can Tie Strings to Federal Grants,” The Conversation, June 1, 2021, http://theconversation.com/congress-cant-do-much-about-fixing-local-police-but-it-can-tie-strings-to-federal-grants-159881 . ( Back to top )
Caitlin Chin, “Highlights: Setting Guidelines for Facial Recognition and Law Enforcement,” The Brookings Institution (blog), December 9, 2019, https://www.brookings.edu/blog/techtank/2019/12/09/highlights-setting-guidelines-for-facial-recognition-and-law-enforcement/ . ( Back to top )
Clare Garvie, Alvaro Bedoya, and Jonathan Frankle, “The Perpetual Line-Up: Unregulated Police Face Recognition in America,” Georgetown Law, Center on Privacy & Technology, October 18, 2016, https://www.perpetuallineup.org/appendix/model-police-use-policy . ( Back to top )
Ibid. ( Back to top)
Rashawn Ray, “Policy Steps for Racially-Equitable Policing,” Testimony before the Virginia Advisory Committee to the U.S. Commission on Civil Rights, July 16, 2021, https://www.brookings.edu/testimonies/policy-steps-for-racially-equitable-policing/ . ( Back to top )
Laura Moy, “A Taxonomy of Police Technology’s Racial Inequity Problems,” U. Ill. L. Rev. 139 (2021), http://dx.doi.org/10.2139/ssrn.3340898 . ( Back to top )
”Cooperation or Resistance?: The Role of Tech Companies in Government Surveillance,” 131 Harv. L. Rev. 1715, 1722 (2018), https://harvardlawreview.org/2018/04/cooperation-or-resistance-the-role-of-tech-companies-in-government-surveillance/ . ( Back to top )
Angel Diaz, “Law Enforcement Access to Smart Devices,” Brennan Center for Justice, December 21, 2020, https://www.brennancenter.org/our-work/research-reports/law-enforcement-access-smart-devices . ( Back to top )
Cameron F. Kerry, John B. Morris, Jr., Caitlin Chin, and Nicol Turner Lee, “Bridging the gaps: A path forward to federal privacy legislation,” The Brookings Institution, June 3, 2020, https://www.brookings.edu/research/bridging-the-gaps-a-path-forward-to-federal-privacy-legislation/ . ( Back to top )
Cathy Cosgrove and Sarah Rippy, “Comparison of Comprehensive Data Privacy Laws in Virginia, California and Colorado,” International Association of Privacy Professionals, July 2021, https://iapp.org/media/pdf/resource_center/comparison_chart_comprehensive_data_privacy_laws_virginia_california_colorado.pdf ; General Data Protection Regulation (2016) https://gdpr-info.eu/ ; Consumer Online Privacy Rights Act, S. 3195, 117th Congress (2021-2022), https://www.congress.gov/bill/117th-congress/senate-bill/3195 ; SAFE DATA Act, S. 2499, 117th Congress (2021-2022), https://www.congress.gov/bill/117th-congress/senate-bill/2499 . ( Back to top )
“Brown Releases New Proposal That Would Protect Consumers’ Privacy from Bad Actors,” Sherrod Brown, U.S. Senator for Ohio, June 18, 2020, https://www.brown.senate.gov/newsroom/press/release/brown-proposal-protect-consumers-privacy ; SAFE DATA Act, S. 2499, 117th Congress (2021-2022), https://www.congress.gov/bill/117th-congress/senate-bill/2499 . ( Back to top )
Dillon Reisman, Jason Schultz, Kate Crawford, and Meredith Whittaker, “Algorithmic impact assessments: A practical framework for public agency accountability,” AI Now Institute, 2018, https://ainowinstitute.org/aiareport2018.pdf . ( Back to top )
“Wyden, Booker and Clarke Introduce Algorithmic Accountability Act of 2022 To Require New Transparency And Accountability For Automated Decision Systems,” Ron Wyden, U.S. Senator for Oregon, February 3, 2022, https://www.wyden.senate.gov/news/press-releases/wyden-booker-and-clarke-introduce-algorithmic-accountability-act-of-2022-to-require-new-transparency-and-accountability-for-automated-decision-systems . ( Back to top )

Governance Studies

Center for Technology Innovation

Nicol Turner Lee

August 6, 2024

Nicol Turner Lee, John Villasenor, Mark Brennan

May 6, 2024

Online Only

2:00 pm - 3:00 pm EDT

IMAGES

(PDF) LAW ENFORCEMENT INTELLIGENCE ANALYSIS AND CRIME ANALYSIS
(PDF) Understanding Law Enforcement Intelligence Processes
US Law enforcement Research paper.docx
Law Enforcement Intelligence Case Study Example
(PDF) Computational Modelling for Law Enforcement Intelligence Analysis
📗 Law Enforcement Profession

VIDEO

HK MP5 Submachine gun West Germany #smg #gun
ANGOLA BOEING 727: VANISHED WITHOUT A TRACE
Law Enforcement Intelligence Training
Powerful Insights into Police Revealed Through Research and Analysis #shorts
IALEIA Update November 2023
Intelligence officer test preparation lecture 02| ppsc/fpsc one paper mcqs syllabus

COMMENTS

PDF Law Enforcement Intelligence
technology that has been used in law enforcement intelligence for decades, this chapter provides a five-point model for technological applications in intelligence, with a core focus on new and emerging technological applications ... centers in addition to a review of the scientific research on the effectiveness of fusion centers. iv .
(PDF) Intelligence-Led Policing in Practice: Reflections From
Abstract. Intelligence-led policing (ILP) is a managerial law enforcement model that seeks to place crime intelligence at the forefront of decision-making. This model has been widely adopted, at ...
Intelligence-Led Policing in Practice: Reflections From Intelligence
Intelligence-led policing (ILP) is a managerial law enforcement model that seeks to place crime intelligence at the forefront of decision-making. This model has been widely adopted, at least notionally, in the United States, United Kingdom, Canada, and Australia.
PDF The Effectiveness of Intelligence Led Policing in Countering Terrorism
Research on intelligence led policing in general is lacking within academia but, it is ... the manpower, funding, or training in place to implement such policies. This paper examines whether intelligence led policing is an effective model to combat terrorism on the local, national, ... law enforcement method compared to a centralized method ...
PDF Understanding Law Enforcement Intelligence Processes
(START) project, "Factors impacting the U.S. Intelligence Process." This research was supported by the Department of Homeland Science and Technology Directorate's ... Understanding Law Enforcement Intelligence Processes 4 efforts through the presence of state and local law enforcement officers who are members of every FBI ...
Digital
In the never-ending search by Law Enforcement Agencies (LEAs) for ways to reduce crime more effectively, the prevention of criminal activity is always considered the ideal solution. Since the 1990s, Intelligence-led Policing (ILP) was implemented in some forms by many LEAs around the world for crime prevention. Along with ILP, LEAs nowadays more and more turn to various new surveillance ...
(Pdf) Law Enforcement Intelligence Analysis and Crime Analysis
Primer description of the meanings, distinctions, roles and relationship of crime analysis and intelligence analysis in law enforcement organizations. Discover the world's research 25+ million members
A quantitative study of the law enforcement in using open source
Open Source Intelligence (OSINT) is an important and powerful approach that law enforcement agencies can use to guide an investigation. In many organizations, there are significant barriers to the adoption of effective OSINT techniques, as well as a failure to adapt fast enough to emerging technologies.
PDF Law Enforcement Intelligence
Law Enforcement Intelligence: A Guide for State, Local, and Tribal Law Enforcement Agencies. is a policy oriented review of current initiatives, national standards, and best practices. The first two chapters provide definitions and context for the current state of law enforcement intelligence. Chapter 2 also provides a discussion of homeland
PDF Introduction: Intelligence-Led Policing, Crime Intelligence and Social
The past two decades have seen considerable change occur within law enforcement and crime intelligence agencies. For example, along with the ... (2009, p. 892) noted that network-related research is a popular topic, with 'the number of articles in the Web of Science on the topic of "social networks" nearly tripling in the past decade ...
Artificial Intelligence, Predictive Policing, and Risk Assessment for
There are widespread concerns about the use of artificial intelligence in law enforcement. Predictive policing and risk assessment are salient examples. Worries include the accuracy of forecasts that guide both activities, the prospect of bias, and an apparent lack of operational transparency. Nearly breathless media coverage of artificial intelligence helps shape the narrative. In this review ...
PDF Intelligence-Led Policing and the New Technologies Adopted by the
3School of Science & Technology, Informatics Studies, Hellenic Open University, Par. Aristotelous 18, 26335 Patra, Greece; [email protected]. *Correspondence: [email protected]. Abstract:In the never-ending search by Law Enforcement Agencies (LEAs) for ways to reduce crime more effectively, the prevention of criminal activity ...
Forensic intelligence: Data analytics as the bridge between forensic
Intelligence takes data from various sources, makes observations and connections, and turns analysis into more than the sum of its parts [39]. Information sharing across jurisdictions is limited both between law enforcement agencies and between crime laboratories, yet the sharing of forensic data sets is critical to gun crime investigations.
The Police Journal: Sage Journals
The Editors of The Police Journal are calling for papers for a Special Issue on the Development and Implementation of Legitimacy Theory in Policing and Criminal Justice. Deadline for Abstracts: December 1, 2024. This journal provides a platform for theory, research and practice to come together to advance all knowledge of different areas and ...
Full article: Policing faces: the present and future of intelligent
In this paper, we bring together a novel analysis unpacking both legal and sociological dimensions of future uses of intelligent facial surveillance (IFS) by law enforcement. The paper provides a case study of live automated facial recognition (LFR) use by police in public spaces in the UK. In Part I, we present insights from 26 frontline ...
PDF Criminal Intelligence
Law enforcement agencies should develop a systematic, scientific, and logical method to comprehensively process information to ensure the most accurate criminal intelligence product is produced and disseminated to the law enforcement professionals who make decisions and/or operationally respond to prevent a criminal threat from reaching fruition.
Exploring and Understanding Law Enforcement's Relationship with
Integrating artificial intelligence (AI) technologies into law enforcement has become a concern of contemporary politics and public discourse. In this paper, we qualitatively examine the perspectives of AI technologies based on 20 semi-structured interviews of law enforcement professionals in North Carolina. We investigate how integrating AI technologies, such as predictive policing and ...
Artificial Intelligence and Law Enforcement
Artificial intelligence is increasingly able to autonomously detect suspicious activities. These activities in turn might warrant further inquiry and eventually trigger preventive or repressive action by law enforcement agencies (Sect. 2).In certain areas, technology already fulfills the task of detecting suspicious activities better than human police officers ever could, especially in ...
Police surveillance and facial recognition: Why data privacy is
From January to June 2020 alone, federal, state, and local law enforcement agencies issued over 112,000 legal requests for data to Apple, Google, Facebook, and Microsoft—three times the number ...
Law Enforcement Use of Artificial Intelligence and Directives in the
December 15, 2023. The use of artificial intelligence (AI) has expanded in a variety of arenas, including by law enforcement. AI has been broadly conceptualized as computerized systems operating in ways often thought to require human intelligence. It is defined in the U.S. Code (15 U.S.C. §9401(3)) as. machine-based system that can, for a ...
PDF Law Enforcement's Usage of Business Intelligence Collaboration
The purpose of this research paper is to gain an understanding of what business intelligence is and to investigate the collaborative usage of business intelligence within law enforcement. First, the varied and many definitions and scopes of business intelligence will be explored and explained, with particular relevancy to law enforcement.
Artificial intelligence & crime prediction: A systematic literature
According to Table 3, analyzing the crime density and neighborhood was the most used approach since 52 research papers utilized this approach, making up 43% of papers. The spatial analysis aspect was performed by 19 research papers, or 16% of all papers. A total of 9 research papers, or 7%, applied human behavior analysis for crime prediction.
Artificial Intelligence and Automated Law Enforcement: A Review Paper
Abstract. This paper discusses the legal issues generated by the introduction of Artificial Intelligence ("AI") and cognitive robotics technologies in the area of law enforcement. This can be referred to as "automated law enforcement". The paper starts by providing a quick reminder on "law enforcement" (1). It moves on to provide an ...
Innovation Helps with Sustainable Business, Law, and Digital
This paper discusses the dispute resolution procedure that innovative digital commerce has adopted for the future for sustainable business. As digital trade becomes increasingly important for economic growth, trade-related disputes must be settled in both business and consumer situations. This study examines the advantages of using digital technology to resolve disputes involving digital trade ...