Towards Effective Human-AI Decision-Making: The Role of Human Learning in Appropriate Reliance on AI Advice
This work addresses the challenge of designing effective human-AI decision-making systems, but it is incremental as it builds on prior research on mental models.
The study tackled the problem of achieving complementary team performance in human-AI collaboration by investigating how human learning mediates appropriate reliance on AI advice, demonstrating this relationship through an experiment with 100 participants.
The true potential of human-AI collaboration lies in exploiting the complementary capabilities of humans and AI to achieve a joint performance superior to that of the individual AI or human, i.e., to achieve complementary team performance (CTP). To realize this complementarity potential, humans need to exercise discretion in following AI 's advice, i.e., appropriately relying on the AI's advice. While previous work has focused on building a mental model of the AI to assess AI recommendations, recent research has shown that the mental model alone cannot explain appropriate reliance. We hypothesize that, in addition to the mental model, human learning is a key mediator of appropriate reliance and, thus, CTP. In this study, we demonstrate the relationship between learning and appropriate reliance in an experiment with 100 participants. This work provides fundamental concepts for analyzing reliance and derives implications for the effective design of human-AI decision-making.