In AI-assisted decision-making, it is crucial but challenging for humans to achieve appropriate reliance on AI. This paper approaches this problem from a human-centered perspective, "human self-confidence calibration". We begin by proposing an analytical framework to highlight the importance of calibrated human self-confidence. In our first study, we explore the relationship between human self-confidence appropriateness and reliance appropriateness. Then in our second study, We propose three calibration mechanisms and compare their effects on humans' self-confidence and user experience. Subsequently, our third study investigates the effects of self-confidence calibration on AI-assisted decision-making. Results show that calibrating human self-confidence enhances human-AI team performance and encourages more rational reliance on AI (in some aspects) compared to uncalibrated baselines. Finally, we discuss our main findings and provide implications for designing future AI-assisted decision-making interfaces.

翻译：在AI辅助决策中，实现人类对AI的恰当依赖既至关重要又充满挑战。本文从"人类自我信心校准"这一以人为本的视角切入该问题。我们首先提出一个分析框架，强调校准后的人类自我信心的重要性。在第一项研究中，我们探讨了人类自我信心恰当性与依赖恰当性之间的关系。随后在第二项研究中，我们提出了三种校准机制，并比较了它们对人类自我信心和用户体验的影响。第三项研究则考察了自我信心校准对AI辅助决策的影响。结果表明，与未校准的基线相比，校准人类自我信心能够提升人机团队绩效，并在某些方面促进对AI更理性的依赖。最后，我们讨论主要发现，并为未来AI辅助决策界面的设计提供启示。