解释性不等于信任，但能温和提升用户信赖度。
Is Trust Correlated With Explainability in AI? A Meta-Analysis
- 90项研究元分析揭示解释性与信任存在中等正相关
- 解释性对信任有促进作用，但非决定性因素
- 适合关注AI伦理、可信系统设计的研究者
本研究通过元分析方法，综合评估了90项现有文献，探讨人工智能（AI）系统的可解释性与用户信任之间的关系。结果表明，两者间存在统计上显著但中等程度的正相关，说明可解释性虽有助于建立信任，却并非唯一或主导因素。研究不仅为可解释人工智能（XAI）领域提供学术贡献，更强调其在医疗、司法等关键领域中推动问责制和增强用户信任的广泛社会技术意义。通过应对算法偏见与伦理透明性挑战，研究呼吁构建公平且可持续的AI应用生态，强调应追求真实持久的可信性，而非仅关注短期信任提升。
原文摘要 · Abstract (English)
This study critically examines the commonly held assumption that explicability in artificial intelligence (AI) systems inherently boosts user trust. Utilizing a meta-analytical approach, we conducted a comprehensive examination of the existing literature to explore the relationship between AI explainability and trust. Our analysis, incorporating data from 90 studies, reveals a statistically significant but moderate positive correlation between the explainability of AI systems and the trust they engender among users. This indicates that while explainability contributes to building trust, it is not the sole or predominant factor in this equation. In addition to academic contributions to the field of Explainable AI (XAI), this research highlights its broader socio-technical implications, particularly in promoting accountability and fostering user trust in critical domains such as healthcare and justice. By addressing challenges like algorithmic bias and ethical transparency, the study underscores the need for equitable and sustainable AI adoption. Rather than focusing solely on immediate trust, we emphasize the normative importance of fostering authentic and enduring trustworthiness in AI systems.
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