In recent years, especially with the development of Generative AI, more and more people seek advice from AI application when they make important decisions like career choice. The trend raises an important question: Do judges prefer to rely on human or AI advice in different advising scenarios? Although this topic has been discussed variously in research on algorithm appreciation and algorithm aversion, there are still some gaps need to be filled. Based on belief revision theory and the judge-advisor system, this study attempts to explore how advice strategy types (clinical vs. actuarial) and feedback inconsistency will affect judges’ perceived advice utilization when the advisor is different (Human vs. AI). To achieve this objective, a scenario-based online experiment will be carried out to collect data and test our research model.
He, Lujun; Zhao, Hongying; and Ming, Qingfei, "Judge’s Advice Utilization: Whose Advice is More Persuasive, AI or Human?" (2023). PACIS 2023 Proceedings. 65.
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