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

TL;DR

This paper provides a comprehensive bibliometric review of over twenty years of empirical research on trust in AI, highlighting gaps, trends, and future research directions to improve understanding and application.

Contribution

It offers a detailed analysis of existing empirical studies on trust in AI, identifying overlooked issues and proposing strategies for future research to deepen understanding.

Findings

Identifies missing perspectives in global trust discussions.

Highlights reliance on exploratory methodologies.

Reveals lack of contextualized theoretical models.

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.