Bridging the Human-AI Fairness Gap: How Providing Reasons Enhances the Perceived Fairness of Public Decision-Making

Arian Henning () and Pascal Langenbach ()
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Arian Henning: Max Planck Institute for Research on Collective Goods, Bonn
Pascal Langenbach: Max Planck Institute for Research on Collective Goods, Bonn

No 2024_11, Discussion Paper Series of the Max Planck Institute for Research on Collective Goods from Max Planck Institute for Research on Collective Goods

Abstract: Automated legal decision-making is often perceived as less fair than its human counterpart. This human-AI fairness gap poses practical challenges for implementing automated systems in the public sector. Drawing on experimental data from 4,250 participants in three public decision-making scenarios, this study examines how different reasoning models influence the perceived fairness of automated and human decision-making. The results show that providing reasons enhances the perceived fairness of decision-making, regardless of whether decisions are made by humans or machines. Moreover, sufficiently individualized reasoning models have a stronger positive impact on the perceived fairness of automated decisions than on the perceived fairness of human decisions. This largely mitigates the human-AI fairness gap. The results thus suggest that well-designed reasons can improve the acceptability of automated governance.

Date: 2024-05, Revised 2025-05-30
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