Fairness, accountability and transparency: Notes on algorithmic decision-making in criminal justice
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Abstract Over the last few years, legal scholars, policy-makers, activists and others have generated a vast and rapidly expanding literature concerning the ethical ramifications of using artificial intelligence, machine learning, big data and predictive software in criminal justice contexts. These concerns can be clustered under the headings of fairness, accountability and transparency. First, can we trust technology to be fair, especially given that the data on which the technology is based are biased in various ways? Second, whom can we blame if the technology goes wrong, as it inevitably will on occasion? Finally, does it matter if we do not know how an algorithm works or, relatedly, cannot understand how it reached its decision? I argue that, while these are serious concerns, they are not irresolvable. More importantly, the very same concerns of fairness, accountability and transparency apply, with even greater urgency, to existing modes of decision-making in criminal justice. The question, hence, is comparative: can algorithmic modes of decision-making improve upon the status quo in criminal justice? There is unlikely to be a categorical answer to this question, although there are some reasons for cautious optimism.
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Utilizing AI fairness to address bias in risk assessment tools ensures that criminal sentencing decisions are fair.
“Utilizing AI fairness to address bias in risk assessment tools ensures that criminal sentencing decisions are fair [88,281].”
From AI Fairness in Data Management and Analytics: A Review on Challenges, Methodologies and Applications (2023) · cites this paper as [281]
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Cites
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
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
Extraneous factors in judicial decisions
Danziger, S., Levav, J., & Avnaim-Pesso, L. (2011) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1018033108 · Cited by 1,431 · 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
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
Against Prediction: Profiling, Policing, and Punishing in an Actuarial Age
Harcourt, B. E. (2007) · bepress Legal Repository · Cited by 610 · see citing works
Human Decisions and Machine Predictions
Kleinberg, J., Lakkaraju, H., Leskovec, J., Ludwig, J., & Mullainathan, S. (2017) · doi:10.3386/w23180 · Cited by 545 · see citing works
The Oxford Handbook of Criminology
Liebling, A., Maruna, S., & McAra, L. (2012) · Oxford University Press eBooks · doi:10.1093/he/9780199590278.001.0001 · Cited by 412 · see citing works
Misdemeanorland: Criminal Courts and Social Control in an Age of Broken Windows Policing
Gherasim, G. C. (2019) · American, British and Canadian Studies · doi:10.2478/abcsj-2019-0025 · Cited by 187 · see citing works
Harcourt, B. E. (2006) · University of Chicago Press eBooks · doi:10.7208/chicago/9780226315997.001.0001 · Cited by 183 · 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
Free at Last? Judicial Discretion and Racial Disparities in Federal Sentencing
Yang, C. (2015) · The Journal of Legal Studies · doi:10.1086/680989 · Cited by 166 · see citing works
Evidence-Based Sentencing and the Scientific Rationalization of Discrimination
Starr, S. B. (2013) · bepress Legal Repository · Cited by 163 · see citing works
Crime, Punishment, and Politics: An Analysis of Political Cycles in Criminal Sentencing
Berdejó, C., & Yuchtman, N. (2012) · The Review of Economics and Statistics · doi:10.1162/rest_a_00296 · Cited by 125 · see citing works
The Social Psychology of Punishment of Crime
Oswald, M., Bieneck, S., & Hupfeld-Heinemann, J. (2009) · Medical Entomology and Zoology · Cited by 123 · see citing works
Criminal Law in the Age of the Administrative State
Chiao, V. (2018) · Oxford University Press eBooks · doi:10.1093/oso/9780190273941.001.0001 · Cited by 117 · see citing works
Self-citationDuff, R. A. (2018) · Oxford University Press eBooks · doi:10.1093/oso/9780199570195.001.0001 · Cited by 115 · see citing works
Race Differences in Drug Offending and Drug Distribution Arrests
Mitchell, O., & Caudy, M. S. (2015) · Crime & Delinquency · doi:10.1177/0011128714568427 · Cited by 76 · see citing works
Duff, R. A. (2006) · Cited by 69 · see citing works
Racial Equity in Algorithmic Criminal Justice
Huq, A. Z. (2018) · eYLS (Yale Law School) · Cited by 69 · see citing works
Machine Learning Forecasts of Risk to Inform Sentencing Decisions
Berk, R. A., & Hyatt, J. M. (2015) · Federal Sentencing Reporter · doi:10.1525/fsr.2015.27.4.222 · Cited by 67 · see citing works
Race, Crime, and Criminal Justice in Canada
Owusu‐Bempah, A., & Wortley, S. (2013) · Oxford University Press eBooks · doi:10.1093/oxfordhb/9780199859016.013.020 · Cited by 57 · see citing works
Silvestri, M., & Crowther-Dowey, C. (2008) · Cited by 50 · see citing works
Misdemeanorland: Criminal Courts and Social Control in an Age of Broken Windows Policing
Kohler‐Hausmann, I. (2018) · Project Muse (Johns Hopkins University) · Cited by 45 · see citing works
Goodman‐Delahunty, J., & Sporer, S. L. (2009) · Australian Journal of Forensic Sciences · doi:10.1080/00450610903391440 · Cited by 37 · see citing works
Predicting Proportionality: The Case for Algorithmic Sentencing
Chiao, V. (2018) · Criminal Justice Ethics · doi:10.1080/0731129x.2018.1552359 · Cited by 30 · see citing works
Self-citation
Flagged first, then by citations
Research works printed but not linked: 2
- Natapoff A (2012) Misdemeanors. Southern California Law Review 85, 101-163.
- Silvestri M and Crowther-Dowey C (2008) Key Approaches to Criminology: Gender & Crime. Los Angeles: SAGE Publications.
Cited by
Sorted by citations
AI Fairness in Data Management and Analytics: A Review on Challenges, Methodologies and Applications
Chen, P., Wu, L., & Wang, L. (2023) · Applied Sciences · doi:10.3390/app131810258 · Cited by 155 · see citing works
L’agentivité algorithmique, fiction futuriste ou impératif de justice procédurale ?
Grozdanovski, L. (2022) · Réseaux · doi:10.3917/res.232.0099 · Cited by 2 · see citing works
- Authors
- Chiao, V.
- Affiliations
- University of Toronto (CA)
- Corresponding author
- Chiao, Vincent
- ORCID
- Chiao, Vincent
- Venue
- International Journal of Law in Context
- Publisher
- Cambridge University Press (CUP)
- Published
- 2019-06
- Volume
- 15
- Issue
- 2
- Pages
- 126-139
- ISSN
- 1744-5523 (ISSN-L)
- 1744-5531
- OpenAlex
- W2951512047
- Cited by
- 72
- References
- 26
- Citations by year
- 2026: 10 · 2025: 10 · 2024: 15 · 2023: 10 · 2022: 12 · 2021: 10 · 2020: 4 · 2019: 1
- FWCI
- 11.80
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- International Journal of Law in Context (publishedVersion)
- TSpace (University of Toronto) (submittedVersion, cc-by-nc-nd)
- Topics
- Social Sciences › Safety Research › Ethics and Social Impacts of AI
- Social Sciences › Political Science and International Relations › Artificial Intelligence in Law
- Social Sciences › Sociology and Political Science › Criminal Justice and Corrections Analysis
- Keywords
- algorithmic decision-making, criminal justice, fairness, accountability, transparency, artificial intelligence, machine learning, predictive policing, algorithmic bias, big data, algorithmic accountability, algorithmic transparency, algorithmic fairness, human decision-making
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