[论文解读] Trust in AI: Progress, Challenges, and Future Directions

Saleh Afroogh, Ali Akbari+3 more|arXiv (Cornell University)|Mar 12, 2024

Explainable Artificial Intelligence (XAI)被引用 11

一句话总结

本文对 AI 中的信任进行了系统性文献综述，提出了信任度量的分类法，并概述了主要的信任破坏因素、信任建立因素以及未来方向。

ABSTRACT

The increasing use of artificial intelligence (AI) systems in our daily life through various applications, services, and products explains the significance of trust/distrust in AI from a user perspective. AI-driven systems (as opposed to other technologies) have ubiquitously diffused in our life not only as some beneficial tools to be used by human agents but also are going to be substitutive agents on our behalf, or manipulative minds that would influence human thought, decision, and agency. Trust/distrust in AI plays the role of a regulator and could significantly control the level of this diffusion, as trust can increase, and distrust may reduce the rate of adoption of AI. Recently, varieties of studies have paid attention to the variant dimension of trust/distrust in AI, and its relevant considerations. In this systematic literature review, after conceptualization of trust in the current AI literature review, we will investigate trust in different types of human-Machine interaction, and its impact on technology acceptance in different domains. In addition to that, we propose a taxonomy of technical (i.e., safety, accuracy, robustness) and non-technical axiological (i.e., ethical, legal, and mixed) trustworthiness metrics, and some trustworthy measurements. Moreover, we examine some major trust-breakers in AI (e.g., autonomy and dignity threat), and trust makers; and propose some future directions and probable solutions for the transition to a trustworthy AI.

研究动机与目标

• 说明 AI 中信任/不信任对用户采用与监管的重要性。
• 综合人机交互中的当前 AI 信任概念。
• 提出技术性与非技术性（价值性/axiological）信任度量的分类。
• 识别主要的信任破坏因素与信任建立因素，并概述可信 AI 的未来方向。

提出的方法

• 对当前 AI 信任文献进行概念综合。
• 将信任度量分为技术性（安全性、准确性、鲁棒性）与非技术性/价值性（伦理、法律、混合）类别。
• 讨论信任测量与评估方法。
• 识别并分析削弱或提升对 AI 系统信任的因素。
• 提出未来方向和转向可信 AI 的潜在解决方案。

实验结果

研究问题

• RQ1 文献中 AI 信任的主导概念与维度是什么？
• RQ2 信任度如何在技术层面和公理性层面进行衡量与评估？
• RQ3 影响 AI 信任的关键因素（信任破坏者与信任建立者）有哪些，以及哪些方向可以提高信任度？

主要发现

• 提出了信任度量的分类法，将技术性（安全、准确、鲁棒）与非技术性（伦理、法律、混合）度量区分开。
• 本文回顾并概念化了在 AI 交互与领域中使用的信任测量。
• 识别并讨论了主要的信任破坏因素（如自主性与尊严威胁等）和信任建立因素。
• 提出未来方向和可能的解决方案，以促进向可信 AI 的转型。