Human AI Complementarity

Human-AI complementarity research focuses on designing AI systems that effectively augment human decision-making, achieving performance exceeding either human or AI alone. Current efforts concentrate on developing algorithms that optimize human-AI interaction for diverse objectives beyond accuracy, including learning and user experience, often employing reinforcement learning and causal inference frameworks to model the impact of AI recommendations on human choices. This field is crucial for building effective AI assistants across various domains, from education to complex decision-making tasks, by understanding how to leverage both human intuition and AI capabilities for superior outcomes.