Public Trust in Artificial Intelligence: An Interdisciplinary Scoping Review

As AI tools are increasingly integrated across sectors with pre-existing trust concerns—including governance, healthcare, and science communication—there is a growing need to understand the role public trust plays in the realization of AI’s benefits and harms. Public trust in AI is necessarily shaped by the technical characteristics of a given AI tool, the institutional characteristics of the sector it is implemented within, as well as the social and political characteristics constituting specific publics. This suggests a need for interdisciplinary research capable of simultaneously accounting for these interdependent factors. However, undertaking this work requires first operating from shared conceptualizations of trust, public trust, and AI. To address these challenges, this research provides an interdisciplinary scoping review designed to map variations in how public trust in AI is defined, explained, and addressed. The research methodology is guided by the PRISMA extension for scoping reviews (PRISMA-ScR). Publications were collected using a search of online databases, which generated 103 results across 9 disciplines, with social sciences, computer science, and medicine being most common. Preliminary results suggest that while common terminology is used to explain and address public trust, specific conceptualizations, normative implications, and proposed interventions vary widely. These variations occur within and between disciplines, suggesting that in addition to interdisciplinary collaboration, there is a need for increased conceptual clarity when engaging with public trust in artificial intelligence.