LAUSR

intention to accept vulnerability based upon positive expectations of the intentions or behavior of another.” Trust is an attitude that an agent will behave as expected and can be relied upon to reach its goal. Trust breaks down after an error or a misunderstanding between the agent and the trusting individual. The psychological state of trust in AI is an emergent property of a complex system, usually involving many cycles of design, training, deployment, measurement of performance, regulation, redesign, and retraining. Trust matters, especially in critical sectors such as healthcare, defense, and security, where duty of care is foremost. Trustworthiness must be planned, rather than an afterthought. We can trust in AI, such as when a doctor uses algorithms to screen medical images.20 We can also trust with AI, such as when journalists reference a social network algorithm to analyze sources of a news story.37 Growing adoption of AI into institutional systems relies on citizens to trust in these systems and have confidence in the way these systems are designed and regulated. Regional approaches for managing trust in AI have recently emerged, leading to different regulatory regimes in the U.S., the European region, and China. We review these regulatory divergences. Within the European region, research programs are examining how trust impacts user acceptance of AI. Examples include the UKRI Trustworthy Autonomous Systems Hub,a the French Confiance. ai project,b and the German AI Breakthrough Hub.c Europe appears to be developing a “third way,” alongside the U.S. and China.19 Healthcare contains many examples of AI applications, including online harm risk identification,24 mental health behavior classification,29 and