User trust in Artificial Intelligence (AI) enabled systems has been increasingly recognized and proven as a key element to fostering adoption. It has been suggested that AI-enabled systems must go beyond technical-centric approaches and towards embracing a more human centric approach, a core principle of the human-computer interaction (HCI) field. This review aims to provide an overview of the user trust definitions, influencing factors, and measurement methods from 23 empirical studies to gather insight for future technical and design strategies, research, and initiatives to calibrate the user AI relationship. The findings confirm that there is more than one way to define trust. Selecting the most appropriate trust definition to depict user trust in a specific context should be the focus instead of comparing definitions. User trust in AI-enabled systems is found to be influenced by three main themes, namely socio-ethical considerations, technical and design features, and user characteristics. User characteristics dominate the findings, reinforcing the importance of user involvement from development through to monitoring of AI enabled systems. In conclusion, user trust needs to be addressed directly in every context where AI-enabled systems are being used or discussed. In addition, calibrating the user-AI relationship requires finding the optimal balance that works for not only the user but also the system.

翻译：用户对人工智能（AI）赋能系统的信任，已日益被认可并证实为促进其采用的关键要素。研究表明，AI赋能系统必须超越以技术为中心的方法，转向拥抱更加以人为本的理念——这是人机交互领域的核心理念。本综述旨在基于23项实证研究，对用户信任的定义、影响因素及测量方法进行概述，从而为未来校准用户与AI关系的技术策略、设计策略、研究方向及行动计划提供洞见。研究发现，信任的定义并非唯一。相较于比较不同定义，重点应在于为特定情境下的用户信任选取最恰当的定义。用户对AI赋能系统的信任主要受三大主题影响：社会伦理考量、技术与设计特征、以及用户特征。其中，用户特征主导了研究结果，进一步强调了从AI系统开发到监控过程中用户参与的重要性。总之，在使用或讨论AI赋能系统的任何情境中，用户信任都需被直接关注。此外，校准用户与AI的关系要求找到不仅对用户、也对系统最优的平衡点。