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Artificial Intelligence, Predictive Policing, and Risk Assessment for Law Enforcement
Abstract
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, we address these issues by first unpacking depictions of artificial intelligence. Its use in predictive policing to forecast crimes in time and space is largely an exercise in spatial statistics that in principle can make policing more effective and more surgical. Its use in criminal justice risk assessment to forecast who will commit crimes is largely an exercise in adaptive, nonparametric regression. It can in principle allow law enforcement agencies to better provide for public safety with the least restrictive means necessary, which can mean far less use of incarceration. None of this is mysterious. Nevertheless, concerns about accuracy, fairness, and transparency are real, and there are tradeoffs between them for which there can be no technical fix. You can't have it all. Solutions will be found through political and legislative processes achieving an acceptable balance between competing priorities.
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“Reliance on baseline recidivism rates and their variability across time periods and jurisdictions, coupled with data quality limitations, imposes a methodological barrier that AI/ML, on its own, cannot overcome (Berk, 2021;Steyerberg, 2019;Weber et al., 2025).”
From Scoping review on the integration of artificial intelligence and machine learning with structured instruments to predict juvenile recidivism (2026) · cites this paper as Berk, 2021
In the citing paper
“In this regard, as Berk (2021) warns, AI/ML models applied to criminology cannot extract a strong predictive signal if it does not exist in the administrative and historical data sets.”
From Scoping review on the integration of artificial intelligence and machine learning with structured instruments to predict juvenile recidivism (2026) · cites this paper as Berk, 2021
In the citing paper
“This predictive ceiling suggests that the performance of risk assessment tools is more dependent on the quality, validity, and structure of the available data than on the sophistication of the algorithm (Berk, 2021;Steyerberg, 2019).”
From Scoping review on the integration of artificial intelligence and machine learning with structured instruments to predict juvenile recidivism (2026) · cites this paper as Berk, 2021
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Metadata
- Authors
- Berk, R. A.
- Affiliations
- University of Pennsylvania (US)
- Corresponding author
- Berk, Richard A.
- ORCID
- Berk, Richard A.
- Venue
- Annual Review of Criminology
- Publisher
- Annual Reviews
- Published
- 2021-01-13
- Volume
- 4
- Issue
- 1
- Pages
- 209-237
- ISSN
- 2572-4568 (ISSN-L)
- OpenAlex
- W3100801413
- Cited by
- 213
- References
- 62
- Citations by year
- 2026: 73 · 2025: 50 · 2024: 37 · 2023: 23 · 2022: 25 · 2021: 4
- FWCI
- 2.55
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Annual Review of Criminology (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Ethics and Social Impacts of AI
- Social Sciences › Law › Artificial Intelligence in Law
- Social Sciences › Political Science and International Relations › Policing Practices and Perceptions
- Keywords
- artificial intelligence, predictive policing, algorithmic bias, algorithmic transparency, algorithmic fairness, crime prediction, nonparametric regression, public safety, law enforcement
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