A Person-Centred Approach to Understanding Trust in Moral Machines

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Artificial intelligence (AI) is increasingly used to perform tasks with a moral dimension, such as prioritisingscarce medical resources. Debates rage about the ethical issues of AI, how we should programme ethical AI,and which ethical values we need to prioritize. But machine morality is as much about human moralpsychology as it is about the philosophical and practical issues of building artificial agents. To reap thebenefits of AI, stakeholders need to be willing to use, adopt, and rely on these systems: they must trust in theAI agents.TRUST-AI draws on psychology and philosophy to explore how and when humans trust AI agents that act as'moral machines'. Drawing from classic models of trust and recent theoretical work from moral psychology onthe complexity of trust in the moral domain, this five-year project explores 1) The characteristics of AI agentsthat predict trust; 2) the individual differences that make us more or less likely to trust AI agents; 3) thesituations where we are more likely to 'outsource' moral decisions to AI agents; and 4) how these findingsshould be used to design AI agents that warrant our trust.My approach is methodologically pluralistic and includes qualitative analysis and natural language processing,behavioural economic games, and in-person behavioural experiments. I develop a customised data collectionplatform that will run online experiments globally in at least 3 different languages, as well as cross-culturalstudies in 9 different countries and a range of experiments with an estimated 29,000 participants across fourwork programmes and 20 studies.These findings will help us understand the how, when, and why people trust AI agents with importanttheoretical and methodological implications for research on the antecedents and consequences of trust in moralmachines.