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

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 4250 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.