通过调节自信度，让人类更合理依赖AI，提升人机协作效率。
The Impact and Feasibility of Self-Confidence Shaping for AI-Assisted Decision-Making
- 设计干预方法，动态校准人类对AI的自信水平。
- 实验显示人机团队性能提升近50%，缓解过度与不足依赖。
- 用情感分析预测自信，为实时干预提供可行路径。
在金融、医疗等高风险领域，人类合理依赖AI决策至关重要但极具挑战。本文从以人为中心的角度，提出一种自信心调节干预策略，旨在将人的自信水平校准至目标范围。通过行为实验（共121名参与者）验证，该方法可使人类-人工智能团队性能提升近50%，有效缓解对AI的过度或不足依赖。研究进一步引入自信心预测任务，发现简单机器学习模型在预测自信心方面达到67%准确率。此外，实验揭示情绪与自信心存在关联，表明通过调整情绪可能实现自信心调控。最后，论文展望了该技术在真实场景中部署的研究方向，以支持高效的人机协作。
原文摘要 · Abstract (English)
In AI-assisted decision-making, it is crucial but challenging for humans to appropriately rely on AI, especially in high-stakes domains such as finance and healthcare. This paper addresses this problem from a human-centered perspective by presenting an intervention for self-confidence shaping, designed to calibrate self-confidence at a targeted level. We first demonstrate the impact of self-confidence shaping by quantifying the upper-bound improvement in human-AI team performance. Our behavioral experiments with 121 participants show that self-confidence shaping can improve human-AI team performance by nearly 50% by mitigating both over- and under-reliance on AI. We then introduce a self-confidence prediction task to identify when our intervention is needed. Our results show that simple machine-learning models achieve 67% accuracy in predicting self-confidence. We further illustrate the feasibility of such interventions. The observed relationship between sentiment and self-confidence suggests that modifying sentiment could be a viable strategy for shaping self-confidence. Finally, we outline future research directions to support the deployment of self-confidence shaping in a real-world scenario for effective human-AI collaboration.
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