A Systematic Literature Review of User Trust in AI-Enabled Systems: An HCI Perspective

Abstract

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. Different contexts and various characteristics of both the users and the systems are also found to influence user trust, highlighting the importance of selecting and tailoring features of the system according to the targeted user group's characteristics. Importantly, socio-ethical considerations can pave the way in making sure that the environment where user-AI interactions happen is sufficiently conducive to establish and maintain a trusted relationship. In measuring user trust, surveys are found to be the most common method followed by interviews and focus groups. 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.

Exploring citizens’ stances on AI in public services: A social contract perspective

“…Trust and transparency do not manifest uniformly across all regions and societies, as they vary depending on the regional and societal context (Robinson, 2020; Bach et al, 2022; Wilson, 2022). These values have undergone evolution and display distinct characteristics, ultimately shaping the prevailing stances toward governments and their institutions.…”

Developing machine learning systems worthy of trust for infection science: a requirement for future implementation into clinical practice

“…However, linking definitions of trust, principles for the development of trustworthy AI systems, and user perspectives on trust is an ongoing area of research. This is because user trust is context specific, and needs to be addressed for the specific domains in which user-AI systems exist ( 48 ). Therefore, addressing trust from the perspective of users is open to future research within the IS domain, particularly the clinical laboratory, where significant potential for human-AI teams exists.…”

Exploring factors influencing user perspective of ChatGPT as a technology that assists in healthcare decision making: A cross sectional survey study

“…However, in the context of AI, this trust may become a liability if users become complacent, overlooking the potential for AI responses to degrade as the data landscape shifts. User trust in such AI and the extent to which they perceive it to be a helpful decision-making assistant depends on multiple factors such as socio-ethical considerations, technical and design features, user characteristics, and expertise [9, 10]. When users are well-versed in the mechanics of ChatGPT and the principles guiding its responses, they can navigate its capabilities with discernment, appropriately integrating it into their decision-making processes.…”