Skip to main content

Advertisement

Log in

Using artificial intelligence to prevent crime: implications for due process and criminal justice

  • Open Forum
  • Published:
AI & SOCIETY Aims and scope Submit manuscript

Abstract

Traditional notions of crime control often position the police against an individual, known or not yet known, who is responsible for the commission of a crime. However, with increasingly sophisticated technology, policing increasingly prioritizes the prevention of crime, making it necessary to ascertain who, or what class of persons, may be the next likely criminal before a crime can be committed, termed predictive policing. This causes a shift from individualized suspicion toward predictive profiling that may sway the expectations of a police patrol. Classically, where a patrol officer forms reasonable suspicion prior to a stop, it is based upon his/her analysis of the situation taken as a whole in context. However, where a predictive profile is employed, information available to the officer accordingly adjusts his/her perception of context and affects the application of the reasonable suspicion standard. This article addresses the way in which new approaches to forming reasonable suspicion affect the due process protection of individuals’ fundamental rights. It argues that while an officer still operates with good faith discretion, using predictive profiling causes reasonable suspicion to be based on an augmented understanding of reality and as a result, due process guarantees are weakened. The rights to non-discrimination and the presumption of innocence are assessed and argued as illustrative of this weakening and shift in policing standards. The article ultimately argues that while predictive policing cannot be categorically labeled as inconsistent with criminal justice, changes as seemingly moderate as the manner in which discretion operates have larger effects on individuals’ rights.

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

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Similar content being viewed by others

Data availability

My manuscript has no associated data.

Notes

  1. See, https://www.shotspotter.com/law-enforcement/patrol-management/.

  2. See, Letellier v. France (1991) 14 EHRR 83 for a discussion of pre-trial detention limits; also Case of Asan Rushti v. Austria App no 28389/95 (ECHR, 21 March 2000) on the role of an acquittal in applying the presumption of innocence; also Allen v. the United Kingdom [GC], no. 25424/09, para 103, 12 July 2013 on applying a wide scope; also Case of Cleve. v. Germany App no 48144/09 (ECHR 15 January 2015) on the role of discontinuance or dismissal.

  3. Like the presumption of innocence is a legal construct that does not imply one must believe an individual to be factually innocent, the presumption of guilt by police does not require that he/she believes the individual affirmatively guilty. Instead, it is the reasonable suspicion of likely guilt, the legal threshold which makes their intervention appropriate.

  4. For an example of individual-based profiling, see < https://www.london.gov.uk/mopac-publications-0/review-mps-gangs-violence-matrix-update#:~:text=The%20Metropolitan%20Police%20Service%20(MPS,gang%20members%20in%20a%20Borough > .

References

  • Barrett L (2017) Reasonably suspicious algorithms: predictive policing at the United States Border. N.Y.U. Review of Law & Social Change 41, no. 3. https://socialchangenyu.com/review/reasonably-suspicious-algorithms-predictive-policing-at-the-united-states-border/

  • Bayamlıoğlu E, Leenes R (2018) The ‘rule of law’ implications of data-driven decision-making: a techno-regulatory perspective. Law Innov Technol 10(2):295–313. https://doi.org/10.1080/17579961.2018.1527475

    Article  Google Scholar 

  • Brennan-Marquez K (2017) ‘Plausible Cause’: explanatory standards in the age of powerful machines. Vanderbilt Law Rev 70(4):1249–1301

    Google Scholar 

  • Campbell L (2013) Criminal labels, the European convention on human rights and the presumption of innocence. Modern Law Rev 76(4):681–707

    Article  Google Scholar 

  • Clark S (2014) The Juror, the citizen, and the human being: the presumption of innocence and the burden of judgment. Crim Law Philos 8:421–429

    Article  Google Scholar 

  • Coomber R, Donnermeyer J, McElrath K, Scott J (2014) Key concepts in crime and society. Sage, Key Concepts

    Google Scholar 

  • Crawford A, Evans K (2007) Crime prevention and community safety. In: The Oxford handbook of criminology, vol 5. Oxford University Press

    Google Scholar 

  • Cyr K (2015) The police officer’s plight: the intersection of policing and the law. Alberta Law Rev 52(4):889–926

    Google Scholar 

  • DeAngelis P (2014) Racial profiling and the presumption of innocence. Netherlands J Legal Philos 43(1):43–58

    Google Scholar 

  • European Agency for Fundamental Rights (2018) Preventing unlawful profiling today and in the future: a guide. https://doi.org/10.2811/73473

  • Ferguson A (2011) Crime mapping and the fourth amendment: redrawing ‘high-crime areas.’ Hastings Law J 63(1):179–232

    Google Scholar 

  • Ferguson (2012) Predictive policing and reasonable suspicion. Emory Law J 62(259):261–325

    Google Scholar 

  • Ferguson AG (2015) Big data and predictive reasonable suspicion. Univ Pa Law Rev 163(2):327–410

    Google Scholar 

  • Ferguson (2017) Policing predictive policing. Washington Univ Law Rev 94(5):1109–1189

    Google Scholar 

  • Gless S (2021) Automated suspicion - and evidence?. In: Presented at the Facial recognition vs. Criminal Justice, Council of Europe AI & Law Webinar Series. https://www.coe.int/en/web/artificial-intelligence/-/ai-law-webinar-9-facial-recognition-vs-criminal-justice?fbclid=IwAR3Y-uYro9TEcar-qr1sJAMJUaThTv9Izhs5L9oNSitOIVaFJuR4Z5sE5Jw

  • Goff P, Kahn KB (2012) Racial bias in policing: why we know less than we should. Soc Issues Policy Rev 6(1):177–210

    Article  Google Scholar 

  • Hadjimatheou K (2017) Surveillance technologies, wrongful conviction, and the presumption of innocence. Philos Technol 30(1):39–54

    Article  Google Scholar 

  • Harcourt B (2015) Risk as a proxy for race: the dangers of risk assessment. Federal Sentencing Report 27(4):237–243

    Article  Google Scholar 

  • Harmon R (2012) The problem of policing. Mich Law Rev 110(5):761–817

    Google Scholar 

  • Innes (2003) Understanding social control: deviance, crime and social order. McGraw-Hill Education, Berlin

    Google Scholar 

  • Innes M (2010) The art, craft, and science of policing. In: The Oxford handbook of empirical legal research. Oxford Handbook. Oxford University Press

    Book  Google Scholar 

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

    Google Scholar 

  • Jones T (2007) Governing security: pluralization, privatization, and polarization in crime control and policing. In: The Oxford handbook of criminology, 5th edn. Oxford Handbook. Oxford University Press

    Google Scholar 

  • Lacey N, Wells C, Quick O (2003) Reconstructing criminal law, 3rd edn. Cambridge University Press

    Google Scholar 

  • Lum K, Isaac W (2016) To predict and serve? Significance 13(5):14–19

    Article  Google Scholar 

  • Lum C, Koper C (2017) Evidence-based policing, translating research into practice. Oxford University Press

    Book  Google Scholar 

  • Lyon D (2002) Surveillance as social sorting, privacy, risk and digital discrimination

  • Maguire M (2008) Criminal investigation and crime control. In handbook of policing. Routledge

    Google Scholar 

  • Mendola M (2016) One step further in the ‘surveillance society’: the case of predictive policing. Leiden University Tech and Law Center

    Google Scholar 

  • Newburn (2008) Police powers. In: Handbook of policing. Routledge, Berlin

    Google Scholar 

  • Newburn T (2008) Crime reduction and community safety. In: Handbook of policing. Routledge

    Google Scholar 

  • Newburn T, Reiner R (2007) Policing and the police. In: The Oxford handbook of criminology, 5th edn. Oxford Handbook. Oxford University Press

    Google Scholar 

  • Nutter P (2019) Machine learning evidence: admissibility and weight. J Constitut Law 21(3):919–958

    Google Scholar 

  • Osoba OA and William W IV (2017) An intelligence in our image: the risks of bias and errors in artificial intelligence. Santa Monica, UNITED STATES: RAND Corporation, The, 2017. http://ebookcentral.proquest.com/lib/unilu-ebooks/detail.action?docID=4848967

  • Packer H (1964) Two models of the criminal process. Univ Pennsylvania Law Rev 113(1):1

    Article  Google Scholar 

  • Perry WL, McInnis B, Price CC, Smith SC, Hollywood JS and Perry WL (2013) Predictive policing: the role of crime forecasting in law enforcement operations. Santa Monica, UNITED STATES: RAND Corporation, The, 2013. http://ebookcentral.proquest.com/lib/unilu-ebooks/detail.action?docID=1437438

  • Picard S, Watkins M, Rempel M and Kerodal A (2019) Beyond the Algorithm; pretrial reform, risk assessment, and racial fairness. New York: Center for Court Innovation https://www.courtinnovation.org/publications/beyond-algorithm.

  • Re R, Solow-Niederman A (2019) Developing artificially intelligent justice. Stanford Tech Law Rev 22(2):242–289

    Google Scholar 

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

    Google Scholar 

  • Sieber U (2018) Alternative systems of crime control; national, transnational, and international dimensions. Reports on research in criminal law, S 161. Dunker & Humblot, Berlin

    Google Scholar 

  • Simmons R (2016) Quantifying criminal procedure: how to unlock the potential of big data in our criminal justice system. Michigan State Law Rev. https://doi.org/10.2139/ssrn.2816006

    Article  Google Scholar 

  • Stern S (2013) Constructive knowledge, probable cause, and administrative decision making. Notre Dame Law Rev 82(3):1085–1142

    Google Scholar 

  • Stewart H (2014) The right to be presumed innocent. Crim Law Philos 8:407–420. https://doi.org/10.1007/s11572-013-9233-x

    Article  Google Scholar 

  • Tilley N (2008) Modern approaches to policing: community, problem-oriented and intelligence-led. In: Handbook of policing. Routledge

    Google Scholar 

  • Welsh B, Farrington D (2012) The Oxford handbook of crime prevention. Oxford University Press

    Google Scholar 

  • Zavrsnik A (2020) Criminal justice, artificial intelligence systems, and human rights. ERA Forum 20:567–583

    Article  Google Scholar 

Download references

Funding

Kelly Blount, JD, is a doctoral researcher at the University of Luxembourg, supported by the Luxembourg National Research Fund (FNR) (PRIDE15/10965388).

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Kelly Blount.

Ethics declarations

Conflict of interest

On behalf of all the authors, the corresponding author states that there is no conflict of interest.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Blount, K. Using artificial intelligence to prevent crime: implications for due process and criminal justice. AI & Soc 39, 359–368 (2024). https://doi.org/10.1007/s00146-022-01513-z

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00146-022-01513-z

Keywords

Navigation