The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.
Algorithms and the Individual in Criminal Law
Abstract
Law-enforcement agencies are increasingly able to leverage crime statistics to make risk predictions for particular individuals, employing a form of inference that some condemn as violating the right to be “treated as an individual.” I suggest that the right encodes agents’ entitlement to a fair distribution of the burdens and benefits of the rule of law. Rather than precluding statistical prediction, it requires that citizens be able to anticipate which variables will be used as predictors and act intentionally to avoid them. Furthermore, it condemns reliance on various indexes of distributive injustice, or unchosen properties, as evidence of law-breaking.
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It is possible for information about protected features to slip in through proxy variables, which correlate strongly with the protected features.
“Unfortunately, it is possible for information about these features to slip in through”
From Efficiency vs. Equity: A Structured Interdisciplinary Review of AI in Criminal Justice Risk Assessments (2026) · cites this paper as [33]
Deep learning has shown no additional predictive power in this particular problem space while suffering from additional needs for training data.
“Deep learning has shown no additional predictive power in this particular problem space while suffering from additional needs for training data [24], while exacerbating existing concerns regarding interpretability and transparency [33].”
From Efficiency vs. Equity: A Structured Interdisciplinary Review of AI in Criminal Justice Risk Assessments (2026) · cites this paper as [33]
Deep learning exacerbates existing concerns regarding interpretability and transparency.
“Deep learning has shown no additional predictive power in this particular problem space while suffering from additional needs for training data [24], while exacerbating existing concerns regarding interpretability and transparency [33].”
From Efficiency vs. Equity: A Structured Interdisciplinary Review of AI in Criminal Justice Risk Assessments (2026) · cites this paper as [33]
The specific biases identified by the authors of the sources are race, gender, age, and economic status.
“The specific biases identified by the authors of our sources are race [14,22,33,46], gender [6,14,47], age [48,49], and economic status [17,[50][51][52].”
From Efficiency vs. Equity: A Structured Interdisciplinary Review of AI in Criminal Justice Risk Assessments (2026) · cites this paper as [33]
There is substantial concern for introducing or perpetuating bias with these products.
“However, there is also substantial concern for introducing or perpetuating bias with these products [33].”
From Efficiency vs. Equity: A Structured Interdisciplinary Review of AI in Criminal Justice Risk Assessments (2026) · cites this paper as [33]
Some products currently offered for use by police departments include Gotham, HunchLab, and PredPol, all made by Palantir, which is headquartered in Miami, FL.
“Some of the products currently offered for use by police departments include: Gotham, HunchLab, and PredPol, all of which were made by Palantir [33], which is headquartered in Miami, FL.”
From Efficiency vs. Equity: A Structured Interdisciplinary Review of AI in Criminal Justice Risk Assessments (2026) · cites this paper as [33]
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The accuracy, fairness, and limits of predicting recidivism
Dressel, J., & Farid, H. (2018) · Science Advances · doi:10.1126/sciadv.aao5580 · Cited by 1,071
Flagged first, then by citations
Research works printed but not linked: 52
- Taslitz, 2010, Police Are People Too: Cognitive Obstacles to, and Opportunities for, Police Getting the Individualized Suspicion Judgment Right, Ohio State Journal of Criminal Law, 8, 7
- Wasserman, 1992, The Morality of Statistical Proof and the Risk of Mistaken Liability, Cardozo Law Review, 13, 935
- 10.1093/arisoc/aoy007 · DOI
- Risinger, 2004, Unsafe Verdicts: The Need for Reformed Standards for the Trial and Review of Factual Innocence Claims, Houston Law Review, 21, 1281
- 10.1007/s11098-018-1137-0 · DOI
- Burgess, 1936, Protecting the Public by Parole and by Parole Prediction, American Institute of Criminal Law and Criminology, 27, 491
- Bolinger, 2021, The Social Epistemology of Legal Trials
- 10.1073/pnas.1912790117 · DOI
- Holder, Eric . 2014. “Remarks to the National Association of Criminal Defense Lawyers 57th Annual Meeting, and 13th State Criminal Justice Network Conference.” Presented in Philadelphia, PA, August 1. · DOI
- 10.1111/papa.12000 · DOI
- 10.1037/a0020473 · DOI · Paper page
- Fuller, 1969, The Morality of Law
- Herrschaft, 2014, Evaluating the Reliability and Validity of the Correctional Offender Management Profiling for Alternative Sanctions (COMPAS) Tool: Implications for Community Corrections Policy
- Dieterich, 2016, COMPAS Risk Scales: Demonstrating Accuracy, Equity, and Predictive Parity
- Moss, 2018b, Probabilistic Knowledge, 10.1093/oso/9780198792154.001.0001 · DOI
- 10.5840/philtopics20184616 · DOI
- Cofone, Ignacio , and Strandberg, Katherine J. . 2019. “Strategic Games and Algorithmic Transparency.” McGill Law Journal. https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3440878. · DOI
- Harris, 1998, Particularized Suspicion, Categorical Judgments: Supreme Court Rhetoric versus Lower Court Reality under Terry v, Ohio.” St. John’s Law Review, 72, 975
- Tillers, 2005, If Wishes Were Horses: Discursive Comments on Attempts to Prevent Individuals from Being Unfairly Burdened by Their Reference Classes, Law, Probability, and Risk, 4, 33, 10.1093/lpr/mgi001 · DOI
- Ewing, 2018, Recent Work on Punishment and Criminogenic Disadvantage, Law and Philosophy, 37, 29, 10.1007/s10982-017-9305-5 · DOI
- Brayne, 2021, Predict and Surveil: Data, Discretion, and the Future of Policing
- 10.1111/j.1088-4963.2007.00106.x · DOI
- Richardson, 2019, Dirty Data, Bad Predictions: How Civil Rights Violations Impact Police Data, Predictive Policing Systems, and Justice, NYU Law Review, 94, 192
- Voigt, 2017, Language from Police Body Camera Footage Shows Racial Disparities in Officer Respect, Proceedings of the National Academy of Sciences, 114, 6521, 10.1073/pnas.1702413114 · DOI · Paper page
- 10.1007/s12103-013-9198-2 · DOI
- 10.1007/s10982-004-7990-3 · DOI
- 10.1007/s11098-013-0182-y · DOI
- O’Neil, 2016, Weapons of Math Destruction
- Schroeder, 2018, When Beliefs Wrong, Philosophical Topics, 46, 115, 10.5840/philtopics20184617 · DOI
- 10.1111/1467-9760.00123 · DOI
- 10.1007/s11229-020-02696-y · DOI
- Joyce, 2005, How Evidence Reflects Probabilities, Philosophical Perspectives, 19, 153, 10.1111/j.1520-8583.2005.00058.x · DOI
- 10.2307/1191633 · DOI
- 10.1080/0020174X.2015.986854 · DOI
- 10.1007/s11229-018-1809-5 · DOI
- Kim, 2020, Entrapment, Culpability, and Legitimacy, Law and Philosophy, 39, 67, 10.1007/s10982-019-09361-7 · DOI
- Amour, 1994, Race Ipsa Loquitur: Of Reasonable Racists, Intelligent Bayesians, and Involuntary Negrophobes, Stanford Law Review, 46, 781, 10.2307/1229093 · DOI
- 10.1007/s11098-018-1219-z · DOI
- 10.1111/jopp.12060 · DOI
- Eubanks, 2018, Automating Inequality: How High-Tech Tools Profile, Police, and Punish the Poor
- Kehl, 2017, Algorithms in the Criminal Justice System: Assessing the Use of Risk Assessments in Sentencing
- Dworkin, 1977, Taking Rights Seriously
- Bambauer, 2015, Hassle, Michigan Law Review, 113, 461
- Harcourt, 2007, Against Prediction: Profiling, Policing, and Punishment in an Actuarial Age
- 10.2307/795726 · DOI
- Kleinberg, 2018, Discrimination in the Age of Algorithms, Journal of Legal Analysis, 10, 113, 10.1093/jla/laz001 · DOI · Paper page
- Tribe, 1971, Trial by Mathematics: Precision and Ritual in the Legal Process, Harvard Law Review, 84, 1329, 10.2307/1339610 · DOI
- Walen, 2011b, A Unified Theory of Detention, with Application to Preventative Detention for Suspected Terrorists, Maryland Law Review, 70, 871
- 10.2139/ssrn.3452030 · DOI
- Walen, 2011a, A Punitive Precondition for Preventative Detention: Lost Status as a Foundation for a Lost Immunity, San Diego Law Review, 48, 1229
- Duff, 1998, Fundamentals of Sentencing Theory, 141
- 10.1007/s10892-010-9095-6 · DOI
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Atkinson, G., & Casagrande, K. (2026) · Information · doi:10.3390/info17060574
Metadata
- Authors
- Jorgensen, R.
- ORCID
- Jorgensen, Renée
- Venue
- Canadian Journal of Philosophy
- Publisher
- Cambridge University Press (CUP)
- Published
- 2021-10-11
- Volume
- 52
- Issue
- 1
- Pages
- 61-77
- ISSN
- 0045-5091
- 1911-0820
- Cited by
- 18
- References
- 1
- Language
- en
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