Twenty-Four Years of Empirical Research on Trust in AI: A Bibliometric Review of Trends, Overlooked Issues, and Future Directions
arXiv:2309.09828 · doi:10.1007/s00146-024-02059-y
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.
Revised version
References in corpus (5)
- To Trust or to Think: Cognitive Forcing Functions Can Reduce Overreliance on AI in AI-assisted Decision-making
- Effect of Confidence and Explanation on Accuracy and Trust Calibration in AI-Assisted Decision Making
- Proxy Tasks and Subjective Measures Can Be Misleading in Evaluating Explainable AI Systems
- Designing AI for Trust and Collaboration in Time-Constrained Medical Decisions: A Sociotechnical Lens
- Politeness Counts: Perceptions of Peacekeeping Robots