Evaluative Dynamics of AI Integration and Expert Performance under Epistemic Dependence across Heterogeneous Stakes

2026-09-19

AI Summary

研究通过医疗案例分析AI与专家工作结合对评价的影响，发现自动AI监督在高风险任务中提升专家评价，并影响系统再使用意愿。

Abstract

AI is increasingly integrated into expert workflows, yet how integration affects perceptions of the expert, AI, and their combination remains unclear in domains where lay users are epistemically dependent on AI-assisted experts. We examine this through a novel controlled medical study (N = 166) and a direct cross-domain analysis with pre-existing academic-advising data (n = 157, combined N = 323). Expert errors reduced evaluations of the human expert across domains. Perceived expertise, however, varied by AI integration strategy in the higher-stakes medical task, where automatic AI oversight produced higher ratings than expert-only or expert-initiated AI. Exploratory ordinal sensitivity analyses identified a performance-contingent reuse pattern, with automatic oversight producing greater intended reuse after successful medical performance. Overall, performance-related recalibration appeared comparatively portable, while integration-structure effects were more selective and context-sensitive. These findings suggest that system designers should consider how AI enters expert workflows, not only whether it is present.

Problem

• AI Integration
• Expert Performance
• Epistemic Dependence
• Heterogeneous Stakes

Innovation

• AI Integration
• Epistemic Dependence
• Automatic AI Oversight
• Expert Performance
• Stake Sensitivity