Optimizing Decision-Maker's Intrinsic Motivation for Effective Human-AI Decision-Making

AI advice is increasingly incorporated into decision-making processes, but evidence suggests that decision-makers often struggle to effectively integrate this advice, leading to tendencies to over-rely or under-utilize AI. My research challenges our field's assumption that decision-makers are inherently motivated to engage with AI. I have discovered that cognitive motivation is essential for individuals to actively engage with, critically evaluate, and effectively incorporate AI advice into decision-making. Thus, I propose that AI-powered decision support systems designed to enhance decision-makers' motivation will improve decision-making efficacy. To this end, I have developed two systems that bolster decision-makers' intrinsic motivation by supporting their competence and autonomy. Empirical results suggest that fostering intrinsic motivation not only leads to enhanced decision-making performance but also improves the subjective experience when compared to no decision assistance or existing decision support paradigms. This research proposes a paradigm shift in the design of AI-assisted decision-making tools, moving towards systems that improve decision performance via enhancing decision-makers' intrinsic motivation to engage with the task and the decision support.