Determinants of self-reported and behavioral trust in an AI advisor within a cooperative problem-solving game

This is the authors' abstract. We don't add key points for this paper.

The widespread adoption of artificially intelligent advisory systems in everyday decision-making situations draws attention to the topic of user trust. Based on psychological theories of trust formation, several key determinants of Trust in Automation (TiA) have been proposed, but received little empirical validation so far. To test them under highly controlled conditions, we implemented an immersive Virtual Reality trust game in which 165 participants solved riddles together with a voice-based AI assistant, evaluated it with respect to several parameters, and decided how much they wanted to rely on its advice. Largely consistent with the TiA model by Körber (2019) , we found perceived system competence, understandability, assumed intentions of developers, and participants’ individual trust propensity to significantly predict user trust in the AI advisor, with the first having the largest influence. Additionally, familiarity moderated the relation between perceived system competence and trust. The created model based on subjective trust measures (self-report scales) was then retested using behavioral trust (number of accepted in-game AI recommendations) as outcome variable. Theoretical, empirical, and practical implications of the results are discussed.