根据用户信任程度动态调整AI建议方式，减少依赖偏差
Adjust for Trust: Mitigating Trust-Induced Inappropriate Reliance on AI Assistance
- 低信任时给解释，高信任时给反向说明
- 降低38%不当依赖，提升20%决策准确率
- 适合医疗、科普等需人机协作场景
信任会影响用户在人机协作决策中对AI建议的依赖程度，低信任导致依赖不足，高信任则引发过度依赖。本文提出，AI应通过信任自适应干预来缓解此类不当依赖。例如，在用户信任较低时提供支持性解释，可促使用户更审慎地考虑建议；在信任较高时提供反向解释，则能抑制盲目信任。在普通人回答科学问题和医生进行医疗诊断两个场景中，我们发现，在低信任阶段提供支持性解释、高信任阶段提供反向解释，可使不当依赖降低最多达38%，决策准确率提升20%。此外，通过自适应插入强制停顿以促进思考，也可有效减少过度依赖。结果表明，根据用户信任水平动态调整AI行为，有助于实现合理依赖，为改善人机协作提供了新路径。
原文摘要 · Abstract (English)
Trust biases how users rely on AI recommendations in AI-assisted decision-making tasks, with low and high levels of trust resulting in increased under- and over-reliance, respectively. We propose that AI assistants should adapt their behavior through trust-adaptive interventions to mitigate such inappropriate reliance. For instance, when user trust is low, providing an explanation can elicit more careful consideration of the assistant's advice by the user. In two decision-making scenarios -- laypeople answering science questions and doctors making medical diagnoses -- we find that providing supporting and counter-explanations during moments of low and high trust, respectively, yields up to 38% reduction in inappropriate reliance and 20% improvement in decision accuracy. We are similarly able to reduce over-reliance by adaptively inserting forced pauses to promote deliberation. Our results highlight how AI adaptation to user trust facilitates appropriate reliance, presenting exciting avenues for improving human-AI collaboration.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。