Paper Number
ECIS2026-2514
Paper Type
SP
Abstract
This study investigates how the disclosed identity of a legal decision-maker (human, AI, or human–AI) shapes perceptions of fairness. We conducted an online experiment (N = 264), where participants evaluated the fairness of a legal decision while receiving different information about its source. Decisions attributed to AI judges were rated significantly less fair on procedural and interactional dimensions, whereas hybrid human–AI decisions were viewed similarly to human decisions. These results suggest that full automation undermines perceived fairness, while human oversight remains critical. Our findings offer implications for designing and governing AI-assisted legal decision-making, indicating that hybrid models may preserve trust while enhancing efficiency.
Recommended Citation
Demir, Sercan; Rose, Stefan; Weinmann, Markus; Liesen, Jennifer; and Li, Xintong, "Ai, Human, And Hybrid Judges: Effects On Fairness Perceptions In Legal Decisions" (2026). ECIS 2026 Proceedings. 19.
https://aisel.aisnet.org/ecis2026/gen_track/gen_track/19
Ai, Human, And Hybrid Judges: Effects On Fairness Perceptions In Legal Decisions
This study investigates how the disclosed identity of a legal decision-maker (human, AI, or human–AI) shapes perceptions of fairness. We conducted an online experiment (N = 264), where participants evaluated the fairness of a legal decision while receiving different information about its source. Decisions attributed to AI judges were rated significantly less fair on procedural and interactional dimensions, whereas hybrid human–AI decisions were viewed similarly to human decisions. These results suggest that full automation undermines perceived fairness, while human oversight remains critical. Our findings offer implications for designing and governing AI-assisted legal decision-making, indicating that hybrid models may preserve trust while enhancing efficiency.