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Accuracy and Fairness for Juvenile Justice Risk Assessments

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  • Richard Berk

Abstract

Risk assessment algorithms used in criminal justice settings are often said to introduce “bias.” But such charges can conflate an algorithm's performance with bias in the data used to train the algorithm with bias in the actions undertaken with an algorithm's output. In this article, algorithms themselves are the focus. Tradeoffs between different kinds of fairness and between fairness and accuracy are illustrated using an algorithmic application to juvenile justice data. Given potential bias in training data, can risk assessment algorithms improve fairness and, if so, with what consequences for accuracy? Although statisticians and computer scientists can document the tradeoffs, they cannot provide technical solutions that satisfy all fairness and accuracy objectives. In the end, it falls to stakeholders to do the required balancing using legal and legislative procedures, just as it always has.

Suggested Citation

  • Richard Berk, 2019. "Accuracy and Fairness for Juvenile Justice Risk Assessments," Journal of Empirical Legal Studies, John Wiley & Sons, vol. 16(1), pages 175-194, March.
  • Handle: RePEc:wly:empleg:v:16:y:2019:i:1:p:175-194
    DOI: 10.1111/jels.12206
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    References listed on IDEAS

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    1. Friedman, Jerome H., 2002. "Stochastic gradient boosting," Computational Statistics & Data Analysis, Elsevier, vol. 38(4), pages 367-378, February.
    2. Richard A. Berk & Susan B. Sorenson & Geoffrey Barnes, 2016. "Forecasting Domestic Violence: A Machine Learning Approach to Help Inform Arraignment Decisions," Journal of Empirical Legal Studies, John Wiley & Sons, vol. 13(1), pages 94-115, March.
    3. Jon Kleinberg & Himabindu Lakkaraju & Jure Leskovec & Jens Ludwig & Sendhil Mullainathan, 2018. "Human Decisions and Machine Predictions," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 133(1), pages 237-293.
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    Cited by:

    1. Liwei Chen & J. J. Po-An Hsieh & Arun Rai, 2022. "How Does Intelligent System Knowledge Empowerment Yield Payoffs? Uncovering the Adaptation Mechanisms and Contingency Role of Work Experience," Information Systems Research, INFORMS, vol. 33(3), pages 1042-1071, September.
    2. Runshan Fu & Ginger Zhe Jin & Meng Liu, 2022. "Does Human-algorithm Feedback Loop Lead to Error Propagation? Evidence from Zillow’s Zestimate," NBER Working Papers 29880, National Bureau of Economic Research, Inc.
    3. Jens Ludwig & Sendhil Mullainathan, 2021. "Fragile Algorithms and Fallible Decision-Makers: Lessons from the Justice System," Journal of Economic Perspectives, American Economic Association, vol. 35(4), pages 71-96, Fall.

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