Judged by robots: Preferences and perceived fairness of algorithmic vs. human punishments

Irene Locci () and Sébastien Massoni ()
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Irene Locci: CRED - Centre de Recherche en Economie et Droit - Université Paris-Panthéon-Assas
Sébastien Massoni: BETA - Bureau d'Économie Théorique et Appliquée - AgroParisTech - UNISTRA - Université de Strasbourg - Université de Haute-Alsace (UHA) - Université de Haute-Alsace (UHA) Mulhouse - Colmar - UL - Université de Lorraine - CNRS - Centre National de la Recherche Scientifique - INRAE - Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement

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Abstract: Automated decision-making is increasingly prevalent, prompting discussions about AI replacing judges in court. This paper explores how machine-made sentencing decisions are perceived through an experimental study using a public good game with punishment. The study examines preferences for human versus automated punishers and the perceived fairness of penalties. Results indicate that rule violators prefer algorithmic punishment when penalty severity is uncertain and violations are significant. While human judges are typically reluctant to delegate, they are more likely to do this when they do not have discretion over the sanction level. Fairness perceptions are similar for both humans and algorithms, except when human judges choose a less severe penalty, which enhances perceived fairness.

Keywords: Experiment; Perceived fairness; Preferences; Automated decision-making; Human-automation interaction; Algorithms (search for similar items in EconPapers)
Date: 2026-09-22
Note: View the original document on HAL open archive server: https://univ-pantheon-assas.hal.science/hal-05758756v1
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