Bridging the Human-Automation 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 decision-making in legal contexts is often perceived as less fair than its human counterpart. This human-automation 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, the study demonstrates that sufficiently individualized reasoning largely mitigates the human-automation fairness gap. The study thus contributes to the understanding of how procedural elements like giving reasons for decisions shape perceptions of automated government and suggests that well-designed reason giving can improve the acceptability of automated decision systems.

Date: 2024-05
New Economics Papers: this item is included in nep-ain and nep-exp
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