研究解释性如何影响用户对AI的信任，发现互动式解释更可信。
Preliminary Quantitative Study on Explainability and Trust in AI Systems
- 通过网页模拟贷款审批，对比不同解释方式的效果。
- 互动式反事实解释显著提升用户信任和参与度。
- 清晰相关解释是建立信任的关键，适合人机交互研究者。
大型AI模型如GPT-4在法律、医疗、金融等关键领域加速应用，引发对信任与透明度的关切。本研究通过量化实验设计，探讨解释性与用户信任之间的关系。利用交互式网页贷款审批模拟，比较从基础特征重要性到交互式反事实等多种解释方式对感知信任的影响。结果表明，互动性提升用户参与度与信心，解释的清晰度与相关性是信任的关键决定因素。研究为以人为中心的可解释AI领域提供了实证支持，揭示了解释性设计对用户认知的可测量影响。
原文摘要 · Abstract (English)
Large-scale AI models such as GPT-4 have accelerated the deployment of artificial intelligence across critical domains including law, healthcare, and finance, raising urgent questions about trust and transparency. This study investigates the relationship between explainability and user trust in AI systems through a quantitative experimental design. Using an interactive, web-based loan approval simulation, we compare how different types of explanations, ranging from basic feature importance to interactive counterfactuals influence perceived trust. Results suggest that interactivity enhances both user engagement and confidence, and that the clarity and relevance of explanations are key determinants of trust. These findings contribute empirical evidence to the growing field of human-centered explainable AI, highlighting measurable effects of explainability design on user perception
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