Three Challenges for AI-Assisted Decision-Making

Artificial Intelligence (AI) has the potential to improve human decision-making by providing decision recommendations and problem-relevant information to assist the human decision-maker. However, a number of challenges remain in fully realizing the human-AI collaborative potential. First, we need to understand the conditions that support complementarity where the human performance assisted by an AI exceeds the performance of an unassisted human or the AI. This requires that humans are able to recognize the situations where the AI should be leveraged as well as new AI systems that learn to complement the human decision-maker. Second, we discuss the need to accurately assess the human mental models of the AI that contain expectations of the AI as well as reliance strategies that depend on self-confidence, AI confidence, past performance, and prior beliefs. Third, we discuss the need to understand the effects of different design choices for the human-AI interaction including the timing of AI assistance as well as the amount of model information to present to the human decision-maker to avoid cognitive overload and ineffective reliance strategies. For each of these three challenges, we present an interdisciplinary perspective of recent empirical and theoretical findings and discuss new research directions.