Interpretable classification models for recidivism prediction
Open accessAbstract
Summary We investigate a long-debated question, which is how to create predictive models of recidivism that are sufficiently accurate, transparent and interpretable to use for decision making. This question is complicated as these models are used to support different decisions, from sentencing, to determining release on probation to allocating preventative social services. Each case might have an objective other than classification accuracy, such as a desired true positive rate TPR or false positive rate FPR. Each (TPR, FPR) pair is a point on the receiver operator characteristic (ROC) curve. We use popular machine learning methods to create models along the full ROC curve on a wide range of recidivism prediction problems. We show that many methods (support vector machines, stochastic gradient boosting and ridge regression) produce equally accurate models along the full ROC curve. However, methods that are designed for interpretability (classification and regression trees and C5.0) cannot be tuned to produce models that are accurate and/or interpretable. To handle this shortcoming, we use a recent method called supersparse linear integer models to produce accurate, transparent and interpretable scoring systems along the full ROC curve. These scoring systems can be used for decision making for many different use cases, since they are just as accurate as the most powerful black box machine learning models for many applications, but completely transparent, and highly interpretable.
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
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?
Caruana et al. (2015) and Zeng et al. (2017) failed to conduct studies with actual users of the system to evaluate the incremental utility of explanations.
“However, both studies failed to conduct studies with actual users of the system to evaluate the incremental utility of explanations in terms of performance improvement in the task.”
From Explainable machine learning for public policy: Use cases, gaps, and research directions (2023) · cites this paper as Zeng et al. (2017)
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Cites
Breiman, L. (2001) · Machine Learning · doi:10.1023/a:1010933404324 · Cited by 133,538 · see citing works
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 · see citing works
Greedy function approximation: A gradient boosting machine
Friedman, J. H. (2001) · The Annals of Statistics · doi:10.1214/aos/1013203451 · Cited by 30,731 · see citing works
A decision-theoretic generalization of on-line learning and an application to boosting
Freund, Y., & Schapire, R. E. (1997) · Journal of Computer and System Sciences · doi:10.1006/jcss.1997.1504 · Cited by 20,466 · see citing works
The magical number seven, plus or minus two: Some limits on our capacity for processing information.
Miller, G. A. (1956) · Psychological Review · doi:10.1037/h0043158 · Cited by 17,812 · see citing works
An Introduction to Support Vector Machines and Other Kernel-based Learning Methods
Cristianini, N., & Shawe-Taylor, J. (2000) · doi:10.1017/cbo9780511801389 · Cited by 13,855 · see citing works
Friedman, J. H. (2002) · Computational Statistics & Data Analysis · doi:10.1016/s0167-9473(01)00065-2 · Cited by 7,067 · see citing works
Kuhn, M., & Johnson, K. (2013) · doi:10.1007/978-1-4614-6849-3 · Cited by 6,788 · cited 1× · see citing works
“0 is an updated version of the popular C4.5 algorithm (Quinlan 2014, Kuhn and Johnson 2013) that can create decision trees and rule sets.”
BackgroundTop 10 algorithms in data mining
Wu, X., Kumar, V., Ross Quinlan, J., Ghosh, J., Yang, Q., Motoda, H., McLachlan, G. J., Ng, A., Liu, B., Yu, P. S., et al. (2008) · Knowledge and Information Systems · doi:10.1007/s10115-007-0114-2 · Cited by 5,667 · see citing works
Statistical Modeling: The Two Cultures (with comments and a rejoinder by the author)
Breiman, L. (2001) · Statistical Science · doi:10.1214/ss/1009213726 · Cited by 4,340 · 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
The robust beauty of improper linear models in decision making.
Dawes, R. M. (1979) · American Psychologist · doi:10.1037/0003-066x.34.7.571 · Cited by 1,970 · see citing works
Very Simple Classification Rules Perform Well on Most Commonly Used Datasets
Holte, R. C. (1993) · Machine Learning · doi:10.1023/a:1022631118932 · Cited by 1,800 · see citing works
Grove, W. M., & Meehl, P. E. (1996) · Psychology, Public Policy, and Law · doi:10.1037/1076-8971.2.2.293 · Cited by 1,457 · see citing works
Lim, T.-S., Loh, W., & Shih, Y. (2000) · Machine Learning · doi:10.1023/a:1007608224229 · Cited by 1,130 · see citing works
Recidivism of Prisoners Released in 1994
Langan, P. A., & Levin, D. J. (2002) · Federal Sentencing Reporter · doi:10.1525/fsr.2002.15.1.58 · Cited by 1,008 · see citing works
The efficacy of violence prediction: a meta-analytic comparison of nine risk assessment tools.
Yang, M., Wong, S. C. P., & Coid, J. (2010) · Psychological bulletin · doi:10.1037/a0020473 · Cited by 708 · see citing works
Comprehensible classification models
Freitas, A. A. (2014) · ACM SIGKDD Explorations Newsletter · doi:10.1145/2594473.2594475 · Cited by 575 · see citing works
A thirty-year follow-up of treatment effects.
McCord, J. (1978) · American Psychologist · doi:10.1037/0003-066x.33.3.284 · Cited by 391 · see citing works
The Spousal Assault Risk Assessment (SARA) Guide: Reliability and Validity in Adult Male Offenders
Kropp, P., & Hart, S. (2000) · Law and Human Behavior · doi:10.1023/a:1005430904495 · Cited by 373 · see citing works
Cures That Harm: Unanticipated Outcomes of Crime Prevention Programs
McCord, J. (2003) · The ANNALS of the American Academy of Political and Social Science · doi:10.1177/0002716202250781 · Cited by 338 · see citing works
Supersparse linear integer models for optimized medical scoring systems
Ustun, B., & Rudin, C. D. (2015) · Machine Learning · doi:10.1007/s10994-015-5528-6 · Cited by 316 · see citing works
Self-citationLeast angle and ℓ1 penalized regression: A review
Hesterberg, T., Choi, N. H., Meier, L., & Fraley, C. (2008) · Statistics Surveys · doi:10.1214/08-ss035 · Cited by 270 · see citing works
Sherman, L. W. (2007) · Journal of Experimental Criminology · doi:10.1007/s11292-007-9044-y · Cited by 183 · see citing works
Statistical Procedures for Forecasting Criminal Behavior
Berk, R. A., & Bleich, J. (2013) · Criminology & Public Policy · doi:10.1111/1745-9133.12047 · Cited by 180 · see citing works
Berk, R., Sherman, L., Barnes, G., Kurtz, E., & Ahlman, L. (2009) · Journal of the Royal Statistical Society Series A: Statistics in Society · doi:10.1111/j.1467-985x.2008.00556.x · Cited by 178 · see citing works
Belfrage, H., Fransson, R., & Strand, S. J. M. (2000) · Journal of Forensic Psychiatry · doi:10.1080/095851800362445 · Cited by 177 · see citing works
Actuarial Sentencing: An “Unsettled” Proposition
Hannah‐Moffat, K. (2012) · Justice Quarterly · doi:10.1080/07418825.2012.682603 · Cited by 166 · see citing works
Tollenaar, N., & van der Heijden, P. G. M. (2012) · Journal of the Royal Statistical Society Series A (Statistics in Society) · doi:10.1111/j.1467-985x.2012.01056.x · Cited by 129 · see citing works
Actuarial Assessment of Risk for Reoffense Among Adult Sex Offenders
Langton, C. M., Barbaree, H. E., Seto, M. C., Peacock, E. J., Harkins, L., & Hansen, K. T. (2007) · Criminal Justice and Behavior · doi:10.1177/0093854806291157 · Cited by 129 · see citing works
Hoffman, P. B. (1994) · Journal of Criminal Justice · doi:10.1016/0047-2352(94)90090-6 · Cited by 129 · see citing works
Clements, C. B. (1996) · Criminal Justice and Behavior · doi:10.1177/0093854896023001009 · Cited by 98 · see citing works
Forecasting Dangerous Inmate Misconduct: An Application of Ensemble Statistical Procedures
Berk, R. A., Kriegler, B., & Baek, J.-H. (2006) · Journal of Quantitative Criminology · doi:10.1007/s10940-006-9005-z · Cited by 82 · see citing works
REVERSAL OF FORTUNE: The Resurgence of Individual Risk Assessment in Criminal Justice
Simon, J. S. (2005) · Annual Review of Law and Social Science · doi:10.1146/annurev.lawsocsci.1.041604.120006 · Cited by 69 · see citing works
Predicting Recidivism in Homicide Offenders Using Classification Tree Analysis
Neuilly, M.-A., Zgoba, K. M., Tita, G., & Lee, S. S. (2011) · Homicide Studies · doi:10.1177/1088767911406867 · Cited by 59 · see citing works
The Inextricable Link Between Age and Criminal History in Sentencing
Bushway, S. D., & Piehl, A. M. (2006) · Crime & Delinquency · doi:10.1177/0011128706294444 · Cited by 55 · see citing works
Asymmetric Loss Functions for Forecasting in Criminal Justice Settings
Berk, R. A. (2010) · Journal of Quantitative Criminology · doi:10.1007/s10940-010-9098-2 · Cited by 51 · see citing works
Developing a Practical Forecasting Screener for Domestic Violence Incidents
Berk, R. A., He, Y., & Sorenson, S. B. (2005) · Evaluation Review · doi:10.1177/0193841x05275333 · Cited by 43 · see citing works
Forecasts of Violence to Inform Sentencing Decisions
Berk, R. A., & Bleich, J. (2013) · Journal of Quantitative Criminology · doi:10.1007/s10940-013-9195-0 · Cited by 43 · see citing works
The Complexities of Prior Record, Race, Ethnicity, and Policy
Crow, M. S. (2008) · Criminal Justice Review · doi:10.1177/0734016808320709 · Cited by 41 · see citing works
Factors for Predicting Parole Success
Borden, H. G. (1928) · Journal of the American Institute of Criminal Law and Criminology · doi:10.2307/1134622 · Cited by 30 · see citing works
Is There Any Logic to Using Logit
Bushway, S. D. (2013) · Criminology & Public Policy · doi:10.1111/1745-9133.12059 · Cited by 20 · see citing works
Estimating the Number of Crimes Averted by Incapacitation: An Information Theoretic Approach
Bhati, A. S. (2007) · Journal of Quantitative Criminology · doi:10.1007/s10940-007-9034-2 · Cited by 17 · see citing works
Elaboration on Two Points Raised in “Classifier Technology and the Illusion of Progress”
Holte, R. C. (2006) · Statistical Science · doi:10.1214/088342306000000033 · Cited by 9 · see citing works
Flagged first, then by citations
Research works printed but not linked: 2
- Andrade, Joel T. Handbook of violence risk assessment and treatment: New approaches for mental health professionals . Springer Publishing Company, 2009.
- Berk, Richard. The role of race in forecasts of violent crime. Race and social problems , 1(4):231-242, 2009.
Cited by
Sorted by citations
Rudin, C. (2019) · Nature Machine Intelligence · doi:10.1038/s42256-019-0048-x · Cited by 10,561 · see citing works
Self-citationThe accuracy, fairness, and limits of predicting recidivism
Dressel, J., & Farid, H. (2018) · Science Advances · doi:10.1126/sciadv.aao5580 · Cited by 1,071 · see citing works
Human Decisions and Machine Predictions
Kleinberg, J., Lakkaraju, H., Leskovec, J., Ludwig, J., & Mullainathan, S. (2018) · The Quarterly Journal of Economics · doi:10.1093/qje/qjx032 · Cited by 1,040 · see citing works
Mittelstadt, B., Russell, C., & Wachter, S. (2019) · Proceedings of the Conference on Fairness, Accountability, and Transparency · doi:10.1145/3287560.3287574 · Cited by 794 · see citing works
'It's Reducing a Human Being to a Percentage': Perceptions of Justice in Algorithmic Decisions
Binns, R., Van Kleek, M., Veale, M., Lyngs, U., Zhao, J., & Shadbolt, N. (2018) · Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems · doi:10.1145/3173574.3173951 · Cited by 691 · see citing works
Kaur, H., Nori, H., Jenkins, S., Caruana, R., Wallach, H., & Wortman Vaughan, J. (2020) · Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems · doi:10.1145/3313831.3376219 · Cited by 564 · see citing works
The threat of algocracy: reality, resistance and accommodation
Danaher, J. (2016) · Philosophy & Technology · doi:10.1007/s13347-015-0211-1 · Cited by 519 · see citing works
Fairness in machine learning: A survey
Caton, S., & Haas, C. (2024) · ACM Computing Surveys · doi:10.1145/3616865 · Cited by 646 · see citing works
Machine learning for medical ultrasound: status, methods, and future opportunities
Brattain, L. J., Telfer, B. A., Dhyani, M., Grajo, J. R., & Samir, A. E. (2018) · Abdominal Radiology · doi:10.1007/s00261-018-1517-0 · Cited by 262 · see citing works
Johndrow, J. E., & Lum, K. (2019) · The Annals of Applied Statistics · doi:10.1214/18-aoas1201 · Cited by 141 · see citing works
Fairness in Machine Learning: A Survey
Caton, S., & Haas, C. (2020) · arXiv · doi:10.48550/arxiv.2010.04053 · Cited by 646 · see citing works
Explaining machine learning models with interactive natural language conversations using TalkToModel
Slack, D., Krishna, S., Lakkaraju, H., & Singh, S. (2023) · Nature Machine Intelligence · doi:10.1038/s42256-023-00692-8 · Cited by 125 · see citing works
Learning Certifiably Optimal Rule Lists for Categorical Data
Angelino, E., Larus-Stone, N., Alabi, D., Seltzer, M., & Rudin, C. (2017) · arXiv · doi:10.48550/arxiv.1704.01701 · Cited by 108 · see citing works
Self-citationAnik, 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
COGAM: Measuring and Moderating Cognitive Load in Machine Learning Model Explanations
Abdul, A., von der Weth, C., Kankanhalli, M., & Lim, B. Y. (2020) · Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems · doi:10.1145/3313831.3376615 · Cited by 96 · see citing works
On the Existence of Simpler Machine Learning Models
Semenova, L., Rudin, C., & Parr, R. (2022) · 2022 ACM Conference on Fairness Accountability and Transparency · doi:10.1145/3531146.3533232 · Cited by 90 · see citing works
Self-citationExplainable machine learning for public policy: Use cases, gaps, and research directions
Amarasinghe, K., Rodolfa, K. T., Lamba, H., & Ghani, R. (2023) · Data & Policy · doi:10.1017/dap.2023.2 · Cited by 73 · see citing works
"Why Should You Trust My Explanation?" Understanding Uncertainty in LIME Explanations
Zhang, Y., Song, K., Sun, Y., Tan, S., & Udell, M. (2019) · arXiv · doi:10.48550/arxiv.1904.12991 · Cited by 68 · see citing works
- Authors
- Zeng, J., Ustun, B., & Rudin, C.
- Affiliations
- Massachusetts Institute of Technology (US)
- Venue
- Journal of the Royal Statistical Society Series A: Statistics in Society
- Publisher
- Oxford University Press (OUP)
- Published
- 2016-09-05
- Volume
- 180
- Issue
- 3
- Pages
- 689-722
- ISSN
- 0964-1998 (ISSN-L)
- 1467-985X
- OpenAlex
- W1746506709
- Cited by
- 220
- References
- 44
- Citations by year
- 2026: 9 · 2025: 11 · 2024: 30 · 2023: 26 · 2022: 25 · 2021: 21 · 2020: 37 · 2019: 27 · 2018: 22 · 2017: 6 · 2016: 4 · 2015: 1 · 2014: 1
- FWCI
- 16.44
- Type
- journal article
- Language
- en
- Open access
- green
- arxiv.org
- Licence
- academic.oup.com/journals/pages/open_access/funder_policies/chorus/standard_publication_model (vor)
- Locations
- Journal of the Royal Statistical Society Series A (Statistics in Society) (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- RePEc: Research Papers in Economics (submittedVersion)
- arXiv (Cornell University)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Explainable Artificial Intelligence (XAI)
- Social Sciences › Safety Research › Ethics and Social Impacts of AI
- Social Sciences › Sociology and Political Science › Criminal Justice and Corrections Analysis
- Keywords
- recidivism prediction, scoring system, ROC curve, diagnostic sensitivity, false positive rate, classification and regression trees, C5.0, support vector machine, stochastic gradient boosting, ridge regression, sentencing, probation
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
