Drivers of trust in AI across the domains of finance, law, and healthcare: a conjoint study

Abstract Artificial intelligence (AI) systems for decision-support are being introduced across multiple domains. One precondition for successful implementation is that those affected by the decisions trust the AI system. In the literature, many drivers of trust in AI have been proposed. In this study, we investigate the relative importance of six potential drivers of trust across three different decision-making domains, i.e., finance, legal, and healthcare in the Danish population. Based on an initial literature review and focus groups we designed a discrete choice study using conjoint analysis. Respondents were presented with a decision involving an AI system and asked to choose which of three possible AI systems they would trust the most, each system being characterized by a combination of levels of six attributes. All participants were presented with six choice situations. The decisions presented in the three domains were chosen to have approximately equal importance based on the focus group discussions. The three decisions were decision-making about a loan for a house purchase, decision-making about guilt in a case of assault, and diagnosis of skin cancer in primary care. Three samples of approximately 300 respondents each, representative of the Danish adult population were recruited through the panel provider Wilke. The main findings are that the most important drivers of trust differ between domains, with professional responsibility for the final decision being most important in finance and legal contexts, and precision being most important in the healthcare domain. Across all three domains, the absence of a particular trust-making feature has a strong negative effect, stronger than the presence of the same feature.