| Autor: |
Tingru Cui, Yixuan Li, Kaiping Chen, Beiley, James, Feng Liu |
| Předmět: |
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| Zdroj: |
Proceedings of the Pacific Asia Conference on Information Systems (PACIS); 2023, p1-9, 9p |
| Abstrakt: |
There is an emerging trend of using artificial intelligence (AI) to support human in decision-making, from employee recruitment to criminal justice. It is important to design and implement human-centric AI, which calls for a thorough empirical understanding of users' fairness perceptions in practical scenarios. Drawing upon the organizational justice theory and explainable artificial intelligence (XAI) literature, we investigate how AI's explanation and task objectivity may jointly influence the three different dimensions of users' perceived fairness, which further lead to their trusting intentions of AI decisionmaking systems. We suggest that XAI positively influences users' perceived distributive, procedural, and interactional fairness as well as it is more effective for objective task than subjective task. We will test the proposed model by conducting a 3x2 experiment. We believe that this study will significantly contribute to the theoretical landscape of XAI and human-AI interaction and provide important practical contributions for AI system designers. [ABSTRACT FROM AUTHOR] |
| Databáze: |
Complementary Index |
| Externí odkaz: |
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