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The Hidden Effects of Algorithmic Recommendations

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  • Alex Albright

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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    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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