2024/10/02 by Michaela Benk, Sophie Kerstan, Florian von Wangenheim +1 · 3 citations
Medicine · Psychology · Social Sciences · #Artificial Intelligence in Healthcare and Education #Ethics and Social Impacts of AI #Human-Automation Interaction and Safety
paper · pdf · doi:10.1007/s00146-024-02059-y
openalex created_date 2024/10/02 · openalex publication_date 2024/10/02 · openalex updated_date 2026/07/28
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.