In Pursuit of Interpretable, Fair and Accurate Machine Learning for Criminal Recidivism Prediction
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In the criminal setting, research has shown that black box models do not perform any better than simpler and interpretable models.
“In the criminal setting, interpretable models are readily available that are small enough to fit on an index card, and research has shown that black box models do not perform any better in criminal law settings than simpler and interpretable models (31).”
From Interpretable algorithmic forensics (2023) · cites this paper as [31]
The deployment of AI/ML in juvenile justice raises challenges regarding traceability.
“As an extension of the demands for fairness, the deployment of AI/ML in juvenile justice raises challenges regarding traceability.”
From Scoping review on the integration of artificial intelligence and machine learning with structured instruments to predict juvenile recidivism (2026) · cites this paper as Wang et al., 2023
The cost of complexity may outweigh the benefit if it reduces the capacity for auditing and practical explanation, especially when the incremental gain in performance is small.
“This requirement is perhaps most relevant when the incremental gain in performance is small, because the cost of complexity may outweigh the benefit if it reduces the capacity for auditing and practical explanation (Rudin, 2019;Wang et al., 2023).”
From Scoping review on the integration of artificial intelligence and machine learning with structured instruments to predict juvenile recidivism (2026) · cites this paper as Wang et al., 2023
Processing architectures can hinder the legal rationale behind decisions and the detection of underlying flaws or biases.
“Since processing architectures can hinder the legal rationale behind decisions and the detection of underlying flaws or biases (Wang et al., 2023), criminology argues that systems must be designed to ensure institutional inspection and control (Završnik, 2021).”
From Scoping review on the integration of artificial intelligence and machine learning with structured instruments to predict juvenile recidivism (2026) · cites this paper as Wang et al., 2023
Cites
Barocas, S., & Selbst, A. D. (2014) · doi:10.2139/ssrn.2477899 · Cited by 3,048 · see citing works
“Clinical versus Actuarial Judgment.”
Dawes, R. M., Faust, D., & Meehl, P. E. (1989) · Science · doi:10.1126/science.2648573 · Cited by 2,298 · see citing works
Fair prediction with disparate impact: a study of bias in recidivism prediction instruments
Chouldechova, A. (2017) · Big Data · doi:10.1089/big.2016.0047 · Cited by 2,159 · see citing works
Fairness definitions explained
Verma, S., & Rubin, J. (2018) · Proceedings of the International Workshop on Software Fairness · doi:10.1145/3194770.3194776 · Cited by 1,124 · see citing works
Fairness in Criminal Justice Risk Assessments: The State of the Art
Berk, R., Heidari, H., Jabbari, S., Kearns, M., & Roth, A. (2021) · Sociological Methods & Research · doi:10.1177/0049124118782533 · Cited by 988 · see citing works
Accurate intelligible models with pairwise interactions
Lou, Y., Caruana, R., Gehrke, J., & Hooker, G. (2013) · Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining · doi:10.1145/2487575.2487579 · Cited by 564 · see citing works
Assessing Risk Assessment in Action
Stevenson, M. T. (2017) · SSRN Electronic Journal · doi:10.2139/ssrn.3016088 · Cited by 195 · see citing works
Sherman, L. W. (2007) · Journal of Experimental Criminology · doi:10.1007/s11292-007-9044-y · Cited by 183 · see citing works
The age of secrecy and unfairness in recidivism prediction
Rudin, C., Wang, C., & Coker, B. (2020) · Harvard Data Science Review · doi:10.1162/99608f92.6ed64b30 · Cited by 177 · see citing works
Self-citationAn impact assessment of machine learning risk forecasts on parole board decisions and recidivism
Berk, R. (2017) · Journal of Experimental Criminology · doi:10.1007/s11292-017-9286-2 · Cited by 169 · see citing works
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Anik, A. I., & Bunt, A. (2021) · Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems · doi:10.1145/3411764.3445736 · Cited by 103 · see citing works
Interpretable generalized additive neural networks
Kraus, M., Tschernutter, D., Weinzierl, S., & Zschech, P. (2024) · European Journal of Operational Research · doi:10.1016/j.ejor.2023.06.032 · Cited by 59 · see citing works
Interpretable algorithmic forensics
Garrett, B. L., & Rudin, C. (2023) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2301842120 · Cited by 46 · see citing works
“In the criminal setting, interpretable models are readily available that are small enough to fit on an index card, and research has shown that black box models do not perform any better in criminal law settings than simpler and interpretable models (31).”
BackgroundSelf-citationAutomating Automaticity: How the Context of Human Choice Affects the Extent of Algorithmic Bias
Agan, A., Davenport, D., Ludwig, J., & Mullainathan, S. (2023) · doi:10.3386/w30981 · Cited by 25 · see citing works
Rountree, L., Lin, Y.-T., Liu, C., Salvatore, M., Admon, A., Nallamothu, B., Singh, K., Basu, A., Bu, F., & Mukherjee, B. (2025) · Online Journal of Public Health Informatics · doi:10.2196/66598 · Cited by 13 · see citing works
Argudo Palacios, A., & Mechraoui, A. (2026) · Cuadernos de RES PUBLICA en derecho y criminología · doi:10.46661/respublica.13244
“Since processing architectures can hinder the legal rationale behind decisions and the detection of underlying flaws or biases (Wang et al., 2023), criminology argues that systems must be designed to ensure institutional inspection and control (Završnik, 2021).”
- Authors
- Wang, C., Han, B., Patel, B., & Rudin, C.
- Affiliations
- The University of Texas at Austin (US)
- University of Washington (US)
- Duke University (US)
- Corresponding author
- Han, Bin
- Venue
- Journal of Quantitative Criminology
- Published
- 2022-03-28
- Volume
- 39
- Issue
- 2
- Pages
- 519-581
- ISSN
- 0748-4518 (ISSN-L)
- 1573-7799
- OpenAlex
- W3023119642
- Cited by
- 101
- References
- 10
- Citations by year
- 2026: 21 · 2025: 27 · 2024: 22 · 2023: 20 · 2022: 5 · 2021: 6
- FWCI
- 21.44
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- Locations
- Journal of Quantitative Criminology (publishedVersion)
- Funders
- Laura and John Arnold Foundation
- Duke University
- Lord Foundation of North Carolina
- Topics
- Social Sciences › Safety Research › Ethics and Social Impacts of AI
- Social Sciences › Sociology and Political Science › Criminal Justice and Corrections Analysis
- Physical Sciences › Computer Science › Artificial Intelligence › Explainable Artificial Intelligence (XAI)
- Keywords
- model interpretability, algorithmic fairness, COMPAS, pretrial risk assessment, model sparsity, model visualization, criminal justice
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