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The Hidden Effects of Algorithmic Recommendations

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Abstract

Algorithms are intended to improve human decisions with data-driven predictions. However, algorithms provide more than just predictions to decision-makers—they often provide explicit recommendations. In this paper, I demonstrate these algorithmic recommendations have significant independent effects on human decisions. I leverage a natural experiment in which algorithmic recommendations were given to bail judges in some cases but not others. Lenient recommendations increased lenient bail decisions by 40% for marginal cases. The results are consistent with algorithmic recommendations making visible mistakes, such as violent rearrest, less costly to judges by providing them reputational cover. In this way, algorithms can affect human decisions by changing incentives, in addition to informing predictions.

Suggested Citation

  • Alex Albright, 2024. "The Hidden Effects of Algorithmic Recommendations," Opportunity and Inclusive Growth Institute Working Papers 104, Federal Reserve Bank of Minneapolis.
    • Handle: RePEc:fip:fedmoi:99090
    DOI: 10.21034/iwp.104
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    References listed on IDEAS

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    1. Will Dobbie & Jacob Goldin & Crystal S. Yang, 2018. "The Effects of Pretrial Detention on Conviction, Future Crime, and Employment: Evidence from Randomly Assigned Judges," American Economic Review, American Economic Association, vol. 108(2), pages 201-240, February.
    2. Sebastian Calonico & Matias D Cattaneo & Max H Farrell, 2020. "Optimal bandwidth choice for robust bias-corrected inference in regression discontinuity designs," The Econometrics Journal, Royal Economic Society, vol. 23(2), pages 192-210.
    3. Jon Kleinberg & Sendhil Mullainathan, 2019. "Simplicity Creates Inequity: Implications for Fairness, Stereotypes, and Interpretability," NBER Working Papers 25854, National Bureau of Economic Research, Inc.
    4. David Almog & Romain Gauriot & Lionel Page & Daniel Martin, 2024. "AI Oversight and Human Mistakes: Evidence from Centre Court," Papers 2401.16754, arXiv.org, revised Feb 2025.
    5. Sendhil Mullainathan & Ziad Obermeyer, 2022. "Diagnosing Physician Error: A Machine Learning Approach to Low-Value Health Care [“The Determinants of Productivity in Medical Testing: Intensity and Allocation of Care,”]," The Quarterly Journal of Economics, Oxford University Press, vol. 137(2), pages 679-727.
    6. Benjamin Feigenberg & Conrad Miller, 2021. "Racial Divisions and Criminal Justice: Evidence from Southern State Courts," American Economic Journal: Economic Policy, American Economic Association, vol. 13(2), pages 207-240, May.
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    More about this item

    Keywords

    Decision-making; algorithm; Algorithmic recommendation; Bail; Criminal justice;
    All these keywords.

    JEL classification:

    • D91 - Microeconomics - - Micro-Based Behavioral Economics - - - Role and Effects of Psychological, Emotional, Social, and Cognitive Factors on Decision Making
    • K42 - Law and Economics - - Legal Procedure, the Legal System, and Illegal Behavior - - - Illegal Behavior and the Enforcement of Law

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