Beyond Recommender: An Exploratory Study of the Effects of Different AI Roles in AI-Assisted Decision Making
This addresses the problem of inappropriate human reliance on AI in decision-making for users of AI-assisted systems, offering insights for adaptive AI design.
The study investigated how different AI roles (Recommender, Analyzer, Devil's Advocate) affect human-AI decision-making, finding that the Recommender role is not always most effective, especially with low AI performance, where the Analyzer role may be preferable.
Artificial Intelligence (AI) is increasingly employed in various decision-making tasks, typically as a Recommender, providing recommendations that the AI deems correct. However, recent studies suggest this may diminish human analytical thinking and lead to humans' inappropriate reliance on AI, impairing the synergy in human-AI teams. In contrast, human advisors in group decision-making perform various roles, such as analyzing alternative options or criticizing decision-makers to encourage their critical thinking. This diversity of roles has not yet been empirically explored in AI assistance. In this paper, we examine three AI roles: Recommender, Analyzer, and Devil's Advocate, and evaluate their effects across two AI performance levels. Our results show each role's distinct strengths and limitations in task performance, reliance appropriateness, and user experience. Notably, the Recommender role is not always the most effective, especially if the AI performance level is low, the Analyzer role may be preferable. These insights offer valuable implications for designing AI assistants with adaptive functional roles according to different situations.