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Fairness in Criminal Justice Risk Assessments: The State of the Art

Author

Listed:
  • Richard Berk
  • Hoda Heidari
  • Shahin Jabbari
  • Michael Kearns
  • Aaron Roth
Abstract
Objectives: Discussions of fairness in criminal justice risk assessments typically lack conceptual precision. Rhetoric too often substitutes for careful analysis. In this article, we seek to clarify the trade-offs between different kinds of fairness and between fairness and accuracy. Methods: We draw on the existing literatures in criminology, computer science, and statistics to provide an integrated examination of fairness and accuracy in criminal justice risk assessments. We also provide an empirical illustration using data from arraignments. Results: We show that there are at least six kinds of fairness, some of which are incompatible with one another and with accuracy. Conclusions: Except in trivial cases, it is impossible to maximize accuracy and fairness at the same time and impossible simultaneously to satisfy all kinds of fairness. In practice, a major complication is different base rates across different legally protected groups. There is a need to consider challenging trade-offs. These lessons apply to applications well beyond criminology where assessments of risk can be used by decision makers. Examples include mortgage lending, employment, college admissions, child welfare, and medical diagnoses.

Suggested Citation

  • Richard Berk & Hoda Heidari & Shahin Jabbari & Michael Kearns & Aaron Roth, 2021. "Fairness in Criminal Justice Risk Assessments: The State of the Art," Sociological Methods & Research, , vol. 50(1), pages 3-44, February.
    • Handle: RePEc:sae:somere:v:50:y:2021:i:1:p:3-44
    DOI: 10.1177/0049124118782533
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    Citations

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    Cited by:

    1. Dalvi-Esfahani, Mohammad & Mosharaf-Dehkordi, Mehdi & Leong, Lam Wai & Ramayah, T. & Jamal Kanaan-Jebna, Abdulkarim M., 2023. "Exploring the drivers of XAI-enhanced clinical decision support systems adoption: Insights from a stimulus-organism-response perspective," Technological Forecasting and Social Change, Elsevier, vol. 195(C).
    2. Nicholas Tenev, 2024. "De-Biasing Models of Biased Decisions: A Comparison of Methods Using Mortgage Application Data," Papers 2405.00910, arXiv.org.
    3. Emanuele Aliverti & Kristian Lum & James E. Johndrow & David B. Dunson, 2021. "Removing the influence of group variables in high‐dimensional predictive modelling," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 184(3), pages 791-811, July.
    4. Christophe Hurlin & Christophe Perignon & Sébastien Saurin, 2021. "The Fairness of Credit Scoring Models," Working Papers hal-03501452, HAL.
    5. Kigerl, Alex & Hamilton, Zachary & Kowalski, Melissa & Mei, Xiaohan, 2022. "The great methods bake-off: Comparing performance of machine learning algorithms," Journal of Criminal Justice, Elsevier, vol. 82(C).
    6. Anna Langenberg & Shih-Chi Ma & Tatiana Ermakova & Benjamin Fabian, 2023. "Formal Group Fairness and Accuracy in Automated Decision Making," Mathematics, MDPI, vol. 11(8), pages 1-25, April.
    7. Friedle, Cosima, 2022. "How Can Fairness Tools Impact the Understanding of Fairness and the Processes Within a Machine Learning Development Team?," Junior Management Science (JUMS), Junior Management Science e. V., vol. 7(5), pages 1289-1300.
    8. Youmi Suk & Kyung T. Han, 2024. "A Psychometric Framework for Evaluating Fairness in Algorithmic Decision Making: Differential Algorithmic Functioning," Journal of Educational and Behavioral Statistics, , vol. 49(2), pages 151-172, April.
    9. Yoan Hermstrüwer & Pascal Langenbach, 2022. "Fair Governance with Humans and Machines," Discussion Paper Series of the Max Planck Institute for Research on Collective Goods 2022_04, Max Planck Institute for Research on Collective Goods, revised 01 Mar 2023.
    10. Arthur Charpentier, 2022. "Quantifying fairness and discrimination in predictive models," Papers 2212.09868, arXiv.org.
    11. Federico Fioravanti & Iyad Rahwan & Fernando Tohmé, 2022. "Properties of Aggregation Operators Relevant for Ethical Decision Making in Artificial Intelligence," Working Papers 177, Red Nacional de Investigadores en Economía (RedNIE).
    12. Bansak, Kirk & Paulson, Elisabeth, 2023. "Public Opinion on Fairness and Efficiency for Algorithmic and Human Decision-Makers," OSF Preprints pghmx, Center for Open Science.
    13. Kozodoi, Nikita & Jacob, Johannes & Lessmann, Stefan, 2022. "Fairness in credit scoring: Assessment, implementation and profit implications," European Journal of Operational Research, Elsevier, vol. 297(3), pages 1083-1094.
    14. Federico Fioravanti & Iyad Rahwan & Fernando Abel Tohm'e, 2022. "Classes of Aggregation Rules for Ethical Decision Making in Automated Systems," Papers 2206.05160, arXiv.org, revised Jun 2023.

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