The Hidden Effects of Algorithmic Recommendations

Alex Albright
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Alex Albright: https://www.minneapolisfed.org/people/alex-albright

No 104, Opportunity and Inclusive Growth Institute Working Papers from Federal Reserve Bank of Minneapolis

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.

Keywords: Decision-making; algorithm; Algorithmic recommendation; Bail; Criminal justice (search for similar items in EconPapers)
JEL-codes: D91 K42 (search for similar items in EconPapers)
Date: 2024-11-14
New Economics Papers: this item is included in nep-ain and nep-law
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Persistent link: https://EconPapers.repec.org/RePEc:fip:fedmoi:99090

DOI: 10.21034/iwp.104

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