我应该相信谁：AI还是我自己？利用人类和AI的正确性可能性来促进人工智能辅助决策的适当信任
Who Should I Trust: AI or Myself? Leveraging Human and AI Correctness Likelihood to Promote Appropriate Trust in AI-Assisted Decision-Making
摘要：
在人工智能辅助决策中，人类决策者必须知道何时信任人工智能，何时信任自己。然而，之前的研究仅基于AI的正确性可能性（CL）的AI信心来校准人类信任，但忽略了人类的CL，阻碍了最佳团队决策。为了缩小这一差距，我们提出了在任务实例级别上基于双方的CL来促进人类的适当信任。我们首先通过近似他们的决策模型和计算他们在类似情况下的潜在表现来模拟人类的CL。我们通过两个初步研究证明了我们的模型的可行性和有效性。然后，我们提出了三个CL开发策略，以校准用户的信任显式/隐式在人工智能辅助决策过程。受试者间实验（N=293）的结果表明，与仅使用AI信心相比，我们的CL开发策略促进了更适当的人类对AI的信任。我们进一步为更具人性化的人工智能辅助决策提供了实际影响。
英文摘要：
In AI-assisted decision-making, it is critical for human decision-makers to know when to trust AI and when to trust themselves. However, prior studies calibrated human trust only based on AI confidence indicating AI's correctness likelihood (CL) but ignored humans' CL, hindering optimal team decision-making. To mitigate this gap, we proposed to promote humans' appropriate trust based on the CL of both sides at a task-instance level. We first modeled humans' CL by approximating their decision-making models and computing their potential performance in similar instances. We demonstrated the feasibility and effectiveness of our model via two preliminary studies. Then, we proposed three CL exploitation strategies to calibrate users' trust explicitly/implicitly in the AI-assisted decision-making process. Results from a between-subjects experiment (N=293) showed that our CL exploitation strategies promoted more appropriate human trust in AI, compared with only using AI confidence. We further provided practical implications for more human-compatible AI-assisted decision-making.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
