The DOI matches its Crossref record; 62 of the 62 references it cites were found in a registry or an index.
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.
Overview
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
In the citing paper
“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
Citations
Cites
Breiman, L. (2001) · Machine Learning · doi:10.1023/a:1010933404324 · Cited by 133,538
Regression shrinkage and selection via the lasso
Tibshirani, R. (1996) · Journal of the Royal Statistical Society Series B: Statistical Methodology · doi:10.1111/j.2517-6161.1996.tb02080.x · Cited by 51,482
The Elements of Statistical Learning
Hastie, T., Tibshirani, R., & Friedman, J. (2009) · Springer Series in Statistics · doi:10.1007/978-0-387-84858-7 · Cited by 25,970
Classification And Regression Trees
Breiman, L., Friedman, J. H., Olshen, R. A., & Stone, C. J. (2017) · doi:10.1201/9781315139470 · Cited by 11,417
I.—COMPUTING MACHINERY AND INTELLIGENCE
TURING, A. M. (1950) · Mind · doi:10.1093/mind/lix.236.433 · Cited by 10,290
Mostly Harmless Econometrics: An Empiricist's Companion
Angrist, J. D., & Pischke, J.-S. (2009) · doi:10.1515/9781400829828 · Cited by 9,467
Microeconometrics: Methods and Applications
Cameron, A. C., & Trivedi, P. K. (2005) · doi:10.1017/cbo9780511811241 · Cited by 8,092
Quantum supremacy using a programmable superconducting processor
Arute, F., Arya, K., Babbush, R., Bacon, D., Bardin, J. C., Barends, R., Biswas, R., Boixo, S., Brandao, F. G. S. L., Buell, D. A., et al. (2019) · Nature · doi:10.1038/s41586-019-1666-5 · Cited by 7,720
Friedman, J. H. (2002) · Computational Statistics & Data Analysis · doi:10.1016/s0167-9473(01)00065-2 · Cited by 7,067
Cressie, N. A. C. (1993) · Wiley Series in Probability and Statistics · doi:10.1002/9781119115151 · Cited by 6,507
Research works printed but not linked: 44
- Ackerman E., 2017, IEEE Spectrum
- Aldor-Noiman S, 2016, Stat. Sin., 26, 1587
- Angwin A, 2016, ProPublica
- Anselin L, 2000, Criminal Justice 2000: Measurement and Analysis of Crime and Justice, 213
- Berk RA., 2018, Machine Learning Forecasts of Risk in Criminal Justice Settings
- Berk RA, 2013, J. Criminol. Public Policy, 12, 515
- Berk RA, 2016, J. Empir. Legal Stud., 13, 95
- Burgess EM., 1928, The Working of the Indeterminate Sentence Law and the Parole System in Illinois, 205
- Caplan JM., 2011, J. Policy Dev. Res., 13, 57
- Cellen-Jones R., 2014, BBC News: Technology
- Chinoy S., 2019, New York Times
- Coglianese C, 2017, Georget. Law J., 105, 1147
- Corbett-Davies S, 2018, The measure and mismeasure of fairness: a critical review of fair machine learning
- Goel S, 2019, Risk-Resil. Res.
- Goodfellow I, 2016, Deep Learning
- Groff ER, 2004, Crime Prev. Stud., 13, 29
- Hamilton M., 2016, Am. Crim. Law Rev., 52, 231
- Helmstetter A, 2003, J. Geophys. Res., 108, 2483
- Hill K., 2020, New York Times, 3
- Hollywood JS, 2019, Real-Time Crime Centers in Chicago
- Huq AZ., 2019, Duke Law J, 68, 1043
- Johnson SD, 2007, Prospective crime mapping in operational context
- Kearns M, Neel S, Roth A, Wu Z. 2018b. Preventing fairness gerrymandering: auditing and learning subgroup fairness. arXiv 1711.05144v4 [cs.LG] · arXiv
- Kessel JM., 2019, New York Times
- Kroll JA, 2017, Univ. Pa. Law Rev., 165, 633
- Lyngstad T, 2017, Predicting future crime at birth
- Malkevitch J., 2007, Taxi! American Mathematical Society
- Mayson SG., 2019, Yale Law J, 128, 2218
- Metz C., 2019, New York Times
- Mitchell M., 2019, New York Times
- Mullainathan S., 2019, New York Times
- Pande V., 2018, New York Times
- Pascanu R, Gulcehere C, Cho K, Begio Y. 2014. How to construct deep recurrent neural networks. arXiv:13126026v5 [cs.NE]
- Pindyck RS, Rubinfeld DL. 1981. Econometric Models and Economic Forecasts . New York: McGraw Hill. 2nd ed.
- Poole D, 1998, Computational Intelligence: A Logic Approach
- Rachlinski JJ, 2009, Notre Dame Law Rev, 84, 1195
- Reiss AJ., 1951, Am. J. Sociol., 17, 268
- Rosser G, 2016, Appl. Spat. Anal., 12, 5
- Smith CS., 2019, New York Times
- Spielkamp M., 2017, MIT Technology Review
- Starr S., 2014, New York Times
- Wang B, Yin P, Bertozzi AL, Brantingham PJ, Osher SJ, Xin J. 2017a. Deep learning for real-time crime forecasting and its ternarization. arXiv:1711.08833v1 [cs.LG] · arXiv
- Wang B, Zhang D, Zhang D, Brantingham PJ, Bettozzi AL. 2017b. Deep learning for real time crime forecasting. arXiv:1707.03340v1 [math.NA] · arXiv
- Weisburd D, 2004, The growth of Compstat in American policing
Cited by
Sorted by citations
The Ethical Implications of Artificial Intelligence (AI) For Meaningful Work
Bankins, S., & Formosa, P. (2023) · Journal of Business Ethics · doi:10.1007/s10551-023-05339-7 · Cited by 633
Shepherd, D. A., & Majchrzak, A. (2022) · Journal of Business Venturing · doi:10.1016/j.jbusvent.2022.106227 · Cited by 329
The Global Governance of Artificial Intelligence: Next Steps for Empirical and Normative Research
Tallberg, J., Erman, E., Furendal, M., Geith, J., Klamberg, M., & Lundgren, M. (2023) · International Studies Review · doi:10.1093/isr/viad040 · Cited by 169
Schiff, K. J., Schiff, D. S., Adams, I. T., McCrain, J., & Mourtgos, S. M. (2023) · Public Administration Review · doi:10.1111/puar.13754 · Cited by 82
Survey on Machine Learning Biases and Mitigation Techniques
Siddique, S., Haque, M. A., George, R., Gupta, K. D., Gupta, D., & Faruk, M. J. H. (2023) · Digital · doi:10.3390/digital4010001 · Cited by 78
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
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Artificial Intelligence, Predictive Policing, and Risk Assessment for Law Enforcement. https://app.cochat.ai/papers/10.1146/annurev-criminol-051520-012342
- The paper
- Berk, R. A. (2021). Artificial Intelligence, Predictive Policing, and Risk Assessment for Law Enforcement. Annual Review of Criminology. https://doi.org/10.1146/annurev-criminol-051520-012342
