The DOI matches its Crossref record; 96 of the 96 references it cites were found in a registry or an index.
RISK, RACE, AND RECIDIVISM: PREDICTIVE BIAS AND DISPARATE IMPACT*
Open accessAbstract
One way to unwind mass incarceration without compromising public safety is to use risk assessment instruments in sentencing and corrections. Although these instruments figure prominently in current reforms, critics argue that benefits in crime control will be offset by an adverse effect on racial minorities. Based on a sample of 34,794 federal offenders, we examine the relationships among race, risk assessment [the Post Conviction Risk Assessment (PCRA)], and future arrest. First, application of well‐established principles of psychological science revealed little evidence of test bias for the PCRA—the instrument strongly predicts arrest for both Black and White offenders, and a given score has essentially the same meaning—that is, the same probability of recidivism—across groups. Second, Black offenders obtain higher average PCRA scores than do White offenders (d = .34; 13.5 percent nonoverlap in groups’ scores), so some applications could create disparate impact. Third, most (66 percent) of the racial difference in PCRA scores is attributable to criminal history—which is already embedded in sentencing guidelines. Finally, criminal history is not a proxy for race, but instead it mediates the relationship between race and future arrest. Data are more helpful than rhetoric if the goal is to improve practice at this opportune moment in history.
Overview
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
Citations
Cites
Statistical Power Analysis for the Behavioral Sciences
Muller, K. E., & Cohen, J. (1989) · Technometrics · doi:10.2307/1270020 · Cited by 99,603
Tabachnick, B. G., & Fidell, L. S. (1983) · Cited by 79,882
Statistical Power Analysis for the Behavioral Sciences (2nd ed.).
Lachenbruch, P. A., & Cohen, J. (1989) · Journal of the American Statistical Association · doi:10.2307/2290095 · Cited by 37,421
The standards for educational and psychological testing.
Eignor, D. R. (2013) · American Psychological Association eBooks · doi:10.1037/14047-013 · Cited by 6,985
Regression standard errors in clustered samples
Rogers, W. H. (1993) · Stata technical bulletin · Cited by 2,694
National Conference of State Legislatures
Clink, K. (2011) · Reference Reviews · doi:10.1108/09504121111114072 · Cited by 2,310
Kraemer, H. C., Stice, E., Kazdin, A., Offord, D., & Kupfer, D. (2001) · American Journal of Psychiatry · doi:10.1176/appi.ajp.158.6.848 · Cited by 1,712
Comparing effect sizes in follow-up studies: ROC Area, Cohen's d, and r.
Rice, M. E., & Harris, G. T. (2005) · Law and Human Behavior · doi:10.1007/s10979-005-6832-7 · Cited by 1,608 · cited 1×
“Minimum AUCs of .56, .64, and .71 correspond to “small,” “medium,” and “large” effect sizes, respectively (see Rice and Harris, 2005).”
STEFFENSMEIER, D., ULMER, J., & KRAMER, J. (1998) · Criminology · doi:10.1111/j.1745-9125.1998.tb01265.x · Cited by 1,592
A META‐ANALYSIS OF THE PREDICTORS OF ADULT OFFENDER RECIDIVISM: WHAT WORKS!*
GENDREAU, P., LITTLE, T., & GOGGIN, C. (1996) · Criminology · doi:10.1111/j.1745-9125.1996.tb01220.x · Cited by 1,244
Research works printed but not linked: 16
- American Law Institute, 2014, Model Penal Code: Sentencing (Tentative Draft No. 3)
- Arnold John, 2015, Fixing justice in America, Politico Magazine
- Blumstein Alfred, 1993, Racial disproportionality of US prison populations revisited, University of Colorado Law Review, 64, 743
- Carson Elizabeth, 2015, Prisoners in 2014
- Durose Matthew, 2014, Recidivism of Prisoners Released in 30 States in 2005: Patterns from 2005 to 2010
- Elek Warren, 2015, Using Risk and Needs Assessment Information at Sentencing: Observations from Ten Jurisdictions
- Ender Philip, 2011, Binary_mediation: Command to Compute Indirect Effect with Binary Mediator and/or Binary Response Variable
- Flores Anthony, Comparing variable and fixed follow‐up outcome periods: Do different methods produce different results?, Criminal Justice and Behavior
- Frase Richard, 2004, The Future of Imprisonment
- Frase Richard, 2009, Crime and Justice
- Lawrence Alison, 2013, Trends in Sentencing and Corrections: State Legislation
- Lowenkamp Christopher T., 2007, Predictive validity of the LSI‐R on a sample of offenders drawn from the records of the Iowa Department of Correction Data Management System, Federal Probation, 71, 25
- Reynolds Cecil R., 2012, Handbook of Psychology, Vol 10, Assessment Psychology
- Sentencing Project News, 2015, Race & Justice News: Risk Assessment or Race Assessment?
- Subramanian Ram, 2014, Recalibrating Justice: A Review of 2013 State Sentencing and Corrections Trends
- Tonry Michael, 2008, In Crime and Justice
Cited by
Sorted by citations
Items 101–200 of 316
Public perception of accuracy-fairness trade-offs in algorithmic decisions in the United States
Mourali, M., Novakowski, D., Pogacar, R., & Brigden, N. (2025) · PLoS ONE · doi:10.1371/journal.pone.0319861 · Cited by 16
Assessing Risk Among White-Collar Offenders Under Federal Supervision in the Community
Harbinson, E., Benson, M. L., & Latessa, E. J. (2018) · Criminal Justice and Behavior · doi:10.1177/0093854818810317 · Cited by 16
Predictive Validity of the YLS/CMI in a Sample of Spanish Young Offenders of Arab Descent
Villanueva, L., Pomares, A. G., & Adrián, J. (2019) · International Journal of Offender Therapy and Comparative Criminology · doi:10.1177/0306624x19834403 · Cited by 16
Algorithmic Audit of Italian Car Insurance: Evidence of Unfairness in Access and Pricing
Fabris, A., Mishler, A., Gottardi, S., Carletti, M., Daicampi, M., Susto, G. A., & Silvello, G. (2021) · Proceedings of the AAAI/ACM Conference on AI Ethics and Society · doi:10.1145/3461702.3462569 · Cited by 16
Baglivio, M. T., & Wolff, K. T. (2018) · Youth Violence and Juvenile Justice · doi:10.1177/1541204018804870 · Cited by 16
Miller, W. T., Campbell, C., Papp, J., & Ruhland, E. L. (2021) · International Journal of Offender Therapy and Comparative Criminology · doi:10.1177/0306624x211022673 · Cited by 15
Inteligencia artificial sostenible y evaluación ética constructiva
Rodríguez, A. L. T. (2022) · Isegoría · doi:10.3989/isegoria.2022.67.10 · Cited by 15
Jourdan, F., Kaninku, T. T., Asher, N. M., Loubes, J., & Risser, L. (2023) · Algorithms · doi:10.3390/a16030174 · Cited by 14
Lamba, H., Rodolfa, K. T., & Ghani, R. (2021) · ACM SIGKDD Explorations Newsletter · doi:10.1145/3468507.3468518 · Cited by 14
The challenges of being imperfect: how do judges and prosecutors deal with sentencing disparity
Plesničar, M. M. (2024) · Frontiers in Sociology · doi:10.3389/fsoc.2024.1488786 · Cited by 13
Dimensionen von Big Data: Eine politikwissenschaftliche Systematisierung
Ulbricht, L., Haunss, S., Hofmann, J., Klinger, U., Passoth, J.-H., Pentzold, C., Schneider, I., Straßheim, H., & Voß, J.-P. (2018) · Technikzukünfte, Wissenschaft und Gesellschaft · doi:10.1007/978-3-658-21665-8_3 · Cited by 13
Meyer, M., Horowitz, A., Marshall, E., & Lum, K. (2022) · ACM Conference on Fairness, Accountability, and Transparency (FAccT) · doi:10.1145/3531146.3533104 · Cited by 13
Affirmative Algorithms: Relational Equality as Algorithmic Fairness
Zhang, M. (2022) · ACM Conference on Fairness, Accountability, and Transparency (FAccT) · doi:10.1145/3531146.3533115 · Cited by 13
Using family network data in child protection services
James, A., McLeod, J. C., Hendy, S. C., Marks, K., Rusu, D., Nik, S. J., & Plank, M. J. (2019) · PLoS ONE · doi:10.1371/journal.pone.0224554 · Cited by 13
Besculides, M., Mazumdar, M., Phlegar, S., Freeman, R., Wilson, S., Joshi, H., Kia, A., & Gorbenko, K. (2023) · JMIR Formative Research · doi:10.2196/42262 · Cited by 12
Racial fairness in violence risk instruments: a review of the literature
Ashford, L., Spivak, B., & Shepherd, S. M. (2021) · Psychology Crime and Law · doi:10.1080/1068316x.2021.1972108 · Cited by 12
Canonico, L. B., McNeese, N. J., & Duncan, C. (2018) · Proceedings of the Human Factors and Ergonomics Society Annual Meeting · doi:10.1177/1541931218621287 · Cited by 12
Kyriazanos, D. M., Thanos, K.-G., & Thomopoulos, S. C. A. (2019) · IEEE Security & Privacy · doi:10.1109/msec.2018.2888777 · Cited by 12
The Predictive Performance of Risk Assessment in Real Life: An External Validation of the MnSTARR
Duwe, G., & Rocque, M. (2019) · Corrections · doi:10.1080/23774657.2019.1682952 · Cited by 12
Stockton, B., Strange, C. C., & Harel, O. (2023) · Journal of Quantitative Criminology · doi:10.1007/s10940-023-09577-w · Cited by 11
Campbell, C., Barnes, A. R., Papp, J., D’Amato, C., Anderson, V. R., & Moses, N. (2020) · Criminal Justice and Behavior · doi:10.1177/0093854820924834 · Cited by 11
Violence risk assessment for young adults receiving treatment for early psychosis
Rolin, S. A., Bareis, N., Bradford, J.-M. E., Rotter, M., Rosenfeld, B. D., Pauselli, L., Compton, M. T., Stroup, T. S., Appelbaum, P. S., & Dixon, L. B. (2021) · International Journal of Law and Psychiatry · doi:10.1016/j.ijlp.2021.101701 · Cited by 11
Shields, M. C. (2021) · Psychiatric Services · doi:10.1176/appi.ps.202000657 · Cited by 11
DeMichele, M. T., & Baumgartner, P. (2020) · Crime & Delinquency · doi:10.1177/0011128720965116 · Cited by 11
Rodolfa, K. T., Lamba, H., & Ghani, R. (2021) · Nature Machine Intelligence · doi:10.1038/s42256-021-00396-x · Cited by 10
The Trouble with Using Risk Assessment Instruments to Quantify the Chance of Future Offending
Goddard, T. (2021) · Sustainability · doi:10.3390/su132111624 · Cited by 10
Racial Bias in Criminal Records
Grunwald, B. (2022) · Journal of Quantitative Criminology · doi:10.1007/s10940-023-09575-y · Cited by 11
Chouhy, C., Swagar, N., Brancale, J. N., Noorman, K., Siennick, S. E., Caswell, J., Bales, W. D., Pesta, G. B., & Blomberg, T. G. (2021) · American Journal of Criminal Justice · doi:10.1007/s12103-021-09619-8 · Cited by 10
Sheeran, A. M., & Heideman, A. J. (2021) · Social Sciences · doi:10.3390/socsci10070261 · Cited by 10
Judicial decision-making in the era of pretrial reform
Foudray, C. M. A., & Lowder, E. M. (2024) · Psychology Crime and Law · doi:10.1080/1068316x.2024.2375225 · Cited by 10
Mental Disorder and Women’s Recidivism: A Meta-Analysis
Pettersen, C., Wanamaker, K. A., Garvey, M. L., Brown, S. L., & Goodwin, J. (2023) · Feminist Criminology · doi:10.1177/15570851231213100 · Cited by 9
X—Why Trust Science? Reliability, Particularity and the Tangle of Science
Cartwright, N. D. (2020) · Proceedings of the Aristotelian Society (Hardback) · doi:10.1093/arisoc/aoaa015 · Cited by 9
Predictive Bias in Juvenile Risk Assessment: Considering Race/Ethnicity and Sex
Childs, K. K., Peck, J. H., & Brady, C. M. (2022) · Crime & Delinquency · doi:10.1177/00111287221143936 · Cited by 9
The Quarantine Model and its Limits
Lavazza, A., Levin, S. M., & Farina, M. (2023) · Philosophia · doi:10.1007/s11406-023-00683-4 · Cited by 9
The disconnect between the goals of trustworthy AI for law enforcement and the EU research agenda
Sanz, B., Fosch‐Villaronga, E., & Belloso, M. L. (2022) · AI and Ethics · doi:10.1007/s43681-022-00235-8 · Cited by 8
McPhee, J., Heilbrun, K., Cubbon, D. N., Soler, M., & Goldstein, N. E. S. (2023) · Law and Human Behavior · doi:10.1037/lhb0000528 · Cited by 8
Robinson, S. L., McCort, C. D., Smirniotis, C., Wintemute, G. J., & Laqueur, H. S. (2024) · Injury Epidemiology · doi:10.1186/s40621-024-00491-8 · Cited by 8
DeMichele, M. T., Silver, I. A., Labrecque, R. M., Dawes, D., Lattimore, P. K., & Tueller, S. (2024) · Criminal Justice and Behavior · doi:10.1177/00938548241233934 · Cited by 8
Designing Social Machines for Tackling Online Disinformation
Wild, A., Ciortea, A., & Mayer, S. (2020) · International World Wide Web Conference (WWW) · doi:10.1145/3366424.3385770 · Cited by 8
Bias detected? An examination of criminal history using the OYAS-DIS for girls and black youth
Miller, W. T., Campbell, C., & Larnell, T. B. (2021) · Journal of Ethnicity in Criminal Justice · doi:10.1080/15377938.2021.1932661 · Cited by 8
Hughes, J. S., Sandel, A., Yelderman, L. A., & Inman, V. (2021) · Basic and Applied Social Psychology · doi:10.1080/01973533.2021.1982714 · Cited by 8
Rolin, S. A., Scodes, J., Dambreville, R., Nossel, I. R., Bello, I., Wall, M. M., Stroup, T. S., Dixon, L. B., & Appelbaum, P. S. (2022) · Community Mental Health Journal · doi:10.1007/s10597-021-00922-6 · Cited by 8
Teeple, S., Chivers, C., Linn, K. A., Halpern, S. D., Eneanya, N. D., Draugelis, M. E., & Courtright, K. R. (2023) · BMJ Quality & Safety · doi:10.1136/bmjqs-2022-015173 · Cited by 7
Do common risk factors for violence generalize across ethnicity?
Monjazeb, S., & Douglas, K. S. (2022) · Psychology Crime and Law · doi:10.1080/1068316x.2022.2097231 · Cited by 7
Predicting risk in criminal justice in the United States: The ProPublica-COMPAS case
Maxwell, W., Beaudouin, V., & Domingues‐Montanari, S. (2023) · Réseaux · doi:10.3917/res.240.0071 · Cited by 7
Williams, K. R., Stansfield, R., & Campbell, J. C. (2021) · Violence Against Women · doi:10.1177/1077801220988347 · Cited by 7
Efficiency and fairness in recurring data-driven risk assessments of violent recidivism
Karimi-Haghighi, M., & Castillo, C. (2021) · ACM Symposium on Applied Computing · doi:10.1145/3412841.3441975 · Cited by 7
Who does and doesn’t pay taxes?
Advani, A. (2017) · doi:10.1920/bn.ifs.2017.bn0218 · Cited by 7
Slota, S. C. (2020) · doi:10.21428/93b2c832.a9b1ae03 · Cited by 6
Helmus, L. M., Ahmed, S., Lee, S. C., & Olver, M. E. (2024) · Current Psychiatry Reports · doi:10.1007/s11920-023-01480-2 · Cited by 6
Song, Y., & Palanisamy, B. (2024) · World Wide Web · doi:10.1007/s11280-024-01312-0 · Cited by 6
The impact of overbooking on a pre-trial risk assessment tool
Lum, K., Boudin, C., & Price, M. (2020) · ACM Conference on Fairness, Accountability, and Transparency (FAccT) · doi:10.1145/3351095.3372846 · Cited by 6
Psychometric Properties of the MacArthur Community Violence Screening Instrument
Cartwright, J. K., Desmarais, S. L., Grimm, K. J., Meade, A. W., & Dorn, R. A. V. (2020) · International Journal of Forensic Mental Health · doi:10.1080/14999013.2020.1718246 · Cited by 6
Evaluating Fairness of Algorithmic Risk Assessment Instruments: The Problem With Forcing Dichotomies
Zottola, S. A., Desmarais, S. L., Lowder, E. M., & Clarke, S. E. D. (2021) · Criminal Justice and Behavior · doi:10.1177/00938548211040544 · Cited by 6
Davis, R. M., Batastini, A. B., Sacco, D. F., Dahlen, E. R., & Jones, A. C. T. (2021) · Legal and Criminological Psychology · doi:10.1111/lcrp.12204 · Cited by 6
Hilton, N. Z., & Radatz, D. L. (2023) · Crime & Delinquency · doi:10.1177/00111287231178679 · Cited by 6
Matz, A. K., Martinez, A. R., & Kujava, E. (2020) · Crime & Delinquency · doi:10.1177/0011128720950023 · Cited by 6
Muñoz, C. G., Perrault, R. T., & Vincent, G. M. (2020) · Youth Violence and Juvenile Justice · doi:10.1177/1541204020954264 · Cited by 6
The Human Factor: Algorithms, Dissenters, and Detention in Immigration Enforcement
Koulish, R. E., & Calvo, E. F. (2021) · Social Science Quarterly · doi:10.1111/ssqu.12957 · Cited by 5
Safeguarding the Quality of Forensic Assessment in Sentencing
der Wolf, M. V. (2022) · Routledge eBooks · doi:10.4324/9781351266482 · Cited by 5
The impacts of length of prison stay on recidivism of non-violent offenders in Oregon
Leymon, M. H., Campbell, C. M., Henning, K., & Renauer, B. (2022) · Journal of Criminal Justice · doi:10.1016/j.jcrimjus.2022.102002 · Cited by 5
Shoham, E., Efodi, R., Haviv, N., & Shader, C. G. (2021) · International Journal of Offender Therapy and Comparative Criminology · doi:10.1177/0306624x211010291 · Cited by 5
Pricing Risk: An XAI Analysis of Irish Car Insurance Premiums
Byrne, A. (2024) · Communications in computer and information science · doi:10.1007/978-3-031-63803-9_17 · Cited by 5
Algorithmic Risk Assessment als Medium des Rechts
Müller, P., & Pöchhacker, N. (2019) · Österreichische Zeitschrift für Soziologie · doi:10.1007/s11614-019-00352-5 · Cited by 5
Reflections on AI for Humanity: Introduction
Braunschweig, B., & Ghallab, M. (2021) · Lecture notes in computer science · doi:10.1007/978-3-030-69128-8_1 · Cited by 5
Pomares, A. G., Villanueva, L., & Adrián, J. E. (2021) · International Journal of Offender Therapy and Comparative Criminology · doi:10.1177/0306624x211022668 · Cited by 5
Unequal Uncertainty: Rethinking Algorithmic Interventions for Mitigating Discrimination from AIPreprint
Sargeant, H., Jorgensen, M., Shah, A., Goring, S., Weller, A., & Bhatt, U. (2025) · arXiv (Cornell University) · doi:10.48550/arxiv.2508.07872 · Cited by 5
Sheppard, K. G., Talaugon, A. R., & Hernandez, J. L. (2024) · Journal of Criminal Justice · doi:10.1016/j.jcrimjus.2024.102262 · Cited by 4
Roskin‐Frazee, A., & Head, C. D. (2025) · CHI Conference Extended Abstracts on Human Factors in Computing Systems (CHI EA) · doi:10.1145/3706599.3720065 · Cited by 4
Building Nondiscriminatory Algorithms in Selected Data
Arnold, D. S., Dobbie, W., & Hull, P. (2025) · American Economic Review Insights · doi:10.1257/aeri.20240249 · Cited by 4
Kroner, D. G., & Derrick, B. (2020) · Assessment · doi:10.1177/1073191120958066 · Cited by 4
Pushing the boundaries of the quarantine model: Philosophical concerns and policy implications
Farina, M., Lavazza, A., & Levin, S. M. (2023) · Diametros · doi:10.33392/diam.1892 · Cited by 4
Peterson‐Badali, M. (2024) · Law and Human Behavior · doi:10.1037/lhb0000578 · Cited by 4
Lee, Y., & SooHyun, O. (2025) · Crime & Delinquency · doi:10.1177/00111287251316515 · Cited by 4
Piotrowski, R. (2019) · Studia Iuridica · doi:10.5604/01.3001.0012.8623 · Cited by 4
Inteligencia artificial, responsabilidad y compromiso cívico y democrático
Rodríguez, A. L. T. (2020) · Repository of Digital Objects for Teaching Research and Culture (University of Valencia) · Cited by 4
Data-Driven Algorithms in Criminal Justice: Predictions as Self-fulfilling Prophecies
Ugwudike, P. (2021) · Cambridge University Press eBooks · doi:10.1017/9781108891325.015 · Cited by 4
Fair Feature Selection with a Lexicographic Multi-objective Genetic Algorithm
Brookhouse, J., & Freitas, A. A. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-14721-0_11 · Cited by 4
Capturing Incarceration: A phenomenological study of Recidivism in Convicted Women
Shahid, S., & Ashraf, M. (2022) · Journal of Research in Psychology · doi:10.31580/jrp.v4i1.2416 · Cited by 4
Sheppard, K. G., Hernandez, J. L., Butts, J., Mendonca, O., & Orange, J. C. (2023) · Criminology & Public Policy · doi:10.1111/1745-9133.12656 · Cited by 5
Indigent Injustice: A Systematic Review and Meta-analysis of People’s Criminal Legal Outcomes
Clarke, S. E. D., Zottola, S. A., McKinsey, E., Kurtz, B., Shao, T. T., Morrissey, B., & Desmarais, S. L. (2024) · Critical Criminology · doi:10.1007/s10612-024-09768-2 · Cited by 4
A Causal Look at Statistical Definitions of Discrimination
Neto, E. C. (2020) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/3394486.3403130 · Cited by 4
Fairness Is More Than Algorithms: Racial Disparities in Time-to-RecidivismPreprint
Han, J. X., Greenewald, K. H., & Shah, D. (2025) · arXiv (Cornell University) · doi:10.48550/arxiv.2504.18629 · Cited by 4
MacIsaac, A., Killen, A., & SCHMIDT, F. H. (2025) · Criminal Justice and Behavior · doi:10.1177/00938548251353755 · Cited by 3
Predictive policing and predictive justice: Ethics, data protection, and the AI act
Gallese, C. (2026) · Computer law & security review · doi:10.1016/j.clsr.2026.106282 · Cited by 3
Risk assessment tool floundering? Let's ask the client to self-predict
Kroner, D. G., Morgan, R. D., Mills, J. F., & Maeda, K. (2020) · International Journal of Law and Psychiatry · doi:10.1016/j.ijlp.2020.101541 · Cited by 3
Clark, V. A. (2022) · Crime & Delinquency · doi:10.1177/00111287221106943 · Cited by 3
Fekih‐Romdhane, F., Hallit, S. R., Cheour, M., & Jahrami, H. (2023) · Springer eBooks · doi:10.1007/978-3-031-31547-3_45 · Cited by 3
Examining the connection between mental illness and recidivism for persons on parole
Applegarth, D. M. (2024) · Journal of Criminal Justice · doi:10.1016/j.jcrimjus.2024.102315 · Cited by 3
Guiding Prosecutorial Decisions with an Interpretable Statistical Model
Lin, Z. J., Chohlas-Wood, A., & Goel, S. (2019) · Proceedings of the AAAI/ACM Conference on AI Ethics and Society · doi:10.1145/3306618.3314235 · Cited by 3
Chaque âge a ses vertus, comme il a ses défauts
Fréchette, J., & Lussier, P. (2022) · Criminologie · doi:10.7202/1093875ar · Cited by 3
Evolutionary Algorithms for Fair Machine Learning
Freitas, A. A., & Brookhouse, J. (2023) · Genetic and evolutionary computation · doi:10.1007/978-981-99-3814-8_17 · Cited by 3
Exploring Reentry Concerns of Incarcerated Individuals with Severe Mental Illness
Applegarth, D. M., Abrams, L. S., & Farabee, D. J. (2024) · Community Mental Health Journal · doi:10.1007/s10597-024-01389-x · Cited by 3
bias goggles: Graph-Based Computation of the Bias of Web Domains Through the Eyes of Users
Papadakos, P., & Konstantakis, G. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-45439-5_52 · Cited by 3
OxRec model for assessing risk of recidivism: ethics – Authors' reply
Fazel, S., Chang, Z., Långström, N., Fanshawe, T. R., & Mallett, S. (2016) · The Lancet Psychiatry · doi:10.1016/s2215-0366(16)30216-4 · Cited by 3
Chadwick, N. (2020) · Carleton University's Institutional Repository (MacOdrum Library, Carleton University) · doi:10.22215/etd/2020-14021 · Cited by 3
Cohen, T. H., & Lowenkamp, C. T. (2018) · SSRN Electronic Journal · doi:10.2139/ssrn.3191533 · Cited by 3
Self-citationAdvancing fairness in natural language processing : from traditional methods to explainability
Jourdan, F. (2024) · theses.fr (ABES) · doi:10.70675/b8535a05z82f1z46f7z9688z3cd35cea8381 · Cited by 3
What are AI systems? Rethinking the core definition in the EU AI Act
Meinel, P., Baum, K. L., Hermanns, H., Pöppelmann, E., Langer, M., & Lauber-Rönsberg, A. (2026) · Computer law & security review · doi:10.1016/j.clsr.2026.106373 · Cited by 2
Differential Prediction by Race in IRAS-PAT Assessments: An Application of Debiasing Strategies
Lawson, S. G., & Lowder, E. M. (2022) · Justice Quarterly · doi:10.1080/07418825.2022.2086481 · Cited by 2
Metadata
- Authors
- SKEEM, J. L., & LOWENKAMP, C. T.
- Affiliations
- University of California, Berkeley (US)
- United States Courts (US)
- Corresponding author
- SKEEM, JENNIFER L.
- Venue
- Criminology
- Publisher
- Wiley
- Published
- 2016-11
- Volume
- 54
- Issue
- 4
- Pages
- 680-712
- ISSN
- 0011-1384 (ISSN-L)
- 1745-9125
- OpenAlex
- W2546836975
- Semantic Scholar
- de4710c8041180b968ada83c5bc4262232953e8f
- Cited by
- 317
- References
- 96
- Citations by year
- 2026: 23 · 2025: 29 · 2024: 34 · 2023: 24 · 2022: 47 · 2021: 49 · 2020: 42 · 2019: 24 · 2018: 24 · 2017: 12 · 2016: 7
- FWCI
- 35.80
- Type
- journal article
- Language
- en
- Open access
- green
- doi.org
- Locations
- Criminology (publishedVersion)
- SSRN Electronic Journal (publishedVersion)
- Topics
- Social Sciences › Sociology and Political Science › Criminal Justice and Corrections Analysis
- Social Sciences › Psychology › Clinical Psychology › Psychopathy, Forensic Psychiatry, Sexual Offending
- Physical Sciences › Computer Science › Artificial Intelligence › Ethics and Social Impacts of AI
- Keywords
- risk assessment tools, predictive bias, indirect discrimination, recidivism, race, criminal history, federal offenders, mass incarceration, sentencing guidelines
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