Abstract
With the advances in AI technology, comparison studies between humans and AI can not only enhance our understanding of information processing mechanisms underlying human cognition but also facilitate our understanding of AI systems’ behaviour and interactions with humans. In particular, explainable AI (XAI) methods, including both computational and experimental methods, can be used to reveal the mechanisms underlying AI’s behaviour and its interactions with humans. This information can be used (1) as computational models to study human behaviour, (2) for updating users’ beliefs about AI during the interactions, and (3) for evaluation purposes to examine potential ethical issues associated with AI adoption. Different AI systems may require different XAI methods to accurately reveal their underlying mechanisms to facilitate the comparisons with humans. Thus, an important future research direction is to develop task-specific XAI methods through interdisciplinary approaches across psychology and AI to benefit both psychological research and the development of ethical AI.