Algorithmic Recommendations When the Stakes Are High: Evidence from Judicial Elections

Victoria Angelova, Will Dobbie and Crystal S. Yang

AEA Papers and Proceedings, 2024, vol. 114, 633-37

Abstract: We ask whether increased public scrutiny leads to the more effective use of predictive algorithms. We focus on the context of bail, where judges face heightened public scrutiny during competitive partisan elections. We find that judges up for reelection are much more likely to follow the algorithmic recommendation to detain high-risk defendants just before an election. However, release decisions return to normal shortly after the election, and there is little change in pretrial misconduct rates, indicating that heightened public scrutiny, at least through competitive partisan elections, will not lead to the more effective use of predictive algorithms in bail.

JEL-codes: C45 D72 K41 (search for similar items in EconPapers)
Date: 2024
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DOI: 10.1257/pandp.20241074

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