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.

对用户信任、影响因素和测量方法进行的 23 个实证研究的综述确认了定义信任的多种方法和用户特征对 AI 启用系统中的信任产生的影响等主题，从而为未来技术和设计策略、研究和举措提供了洞察。