Twenty-Four Years of Empirical Research on Trust in AI: A Bibliometric Review of Trends, Overlooked Issues, and Future Directions

TL;DR

A comprehensive bibliometric analysis, supplemented with a qualitative content analysis of over two decades of empirical research measuring trust in AI reveals several “elephants in the room” pertaining to missing perspectives in global discussions on trust in AI, a lack of contextualized theoretical models and a reliance on exploratory methodologies.

Abstract

Trust is widely regarded as a critical component to building artificial intelligence (AI) systems that people will use and safely rely upon. As research in this area continues to evolve, it becomes imperative that the research community synchronizes its empirical efforts and aligns on the path toward effective knowledge creation. To lay the groundwork toward achieving this objective, we performed a comprehensive bibliometric analysis, supplemented with a qualitative content analysis of over two decades of empirical research measuring trust in AI, comprising 1'156 core articles and 36'306 cited articles across multiple disciplines. Our analysis reveals several "elephants in the room" pertaining to missing perspectives in global discussions on trust in AI, a lack of contextualized theoretical models and a reliance on exploratory methodologies. We highlight strategies for the empirical research community that are aimed at fostering an in-depth understanding of trust in AI.