Who Needs What Explanation? How User Traits Affect Explanation Effectiveness in AI-Assisted Decision-Making

Can personalized AI explanations improve human-AI team performance? Motivated by research on individual differences in cognitive science, we examine whether user characteristics influence the effectiveness of AI explanations in AI-assisted decision making. We study this question through preregistered experiments in two tasks. In a sentiment-analysis task, we find that individual differences in user characteristics shape how users respond to explanations, but these differences do not lead to human-AI complementarity, where the joint performance of humans and AI exceeds that of either alone. Motivated by this limitation, we design a new geography-guessing task in which humans and AI possess complementary strengths. In this setting, we again observe that user characteristics interact with explanation types, and now these effects also contribute to complementarity. These results suggest that tailoring explanations to individual users can improve performance and provide valuable insights into how personalization may enhance human-AI collaboration.