Paper Number
ECIS2026-1211
Paper Type
CRP
Abstract
Hybrid decision-making, combining humans and artificial intelligence (AI), is commonly viewed as offering the “best of both worlds”. Yet, understanding how stakeholders perceive its fairness re-mains limited. While studies have primarily explored fairness in human- and AI-only configurations, we examined decision recipients’ perceptions of procedural justice in hybrid decision-making with and without explanations. The results of our online experiment (N = 224) involving ethically sensi-tive decisions indicate that the hybrid configuration scores lower in procedural justice than human- or AI-only systems. Although explanations partially improve perceived procedural justice across all configurations, they do not offset hybrid’s lower ratings. Perceived benevolence largely explains these differences, highlighting the need to preserve human qualities when designing hybrid systems. We introduce the concept of Hybrid Dysphoria, which describes discomfort stemming from the am-biguity of human-AI collaboration. This finding contributes to the understanding of fairness in hybrid decision-making and offers implications for responsible AI integration.
Recommended Citation
Merz, Simon; Shollo, Arisa; and Hopf, Konstantin, "Procedural Justice and The Limits Of Explanations In Human-AI Decision-Making" (2026). ECIS 2026 Proceedings. 3.
https://aisel.aisnet.org/ecis2026/ai_anthro/ai_anthro/3
Procedural Justice and The Limits Of Explanations In Human-AI Decision-Making
Hybrid decision-making, combining humans and artificial intelligence (AI), is commonly viewed as offering the “best of both worlds”. Yet, understanding how stakeholders perceive its fairness re-mains limited. While studies have primarily explored fairness in human- and AI-only configurations, we examined decision recipients’ perceptions of procedural justice in hybrid decision-making with and without explanations. The results of our online experiment (N = 224) involving ethically sensi-tive decisions indicate that the hybrid configuration scores lower in procedural justice than human- or AI-only systems. Although explanations partially improve perceived procedural justice across all configurations, they do not offset hybrid’s lower ratings. Perceived benevolence largely explains these differences, highlighting the need to preserve human qualities when designing hybrid systems. We introduce the concept of Hybrid Dysphoria, which describes discomfort stemming from the am-biguity of human-AI collaboration. This finding contributes to the understanding of fairness in hybrid decision-making and offers implications for responsible AI integration.
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