Learning to Decide with AI Assistance under Human-Alignment

The paper presents a theoretical framework for understanding the alignment between AI confidence and human decision-maker confidence in AI-assisted decision-making, specifically in binary prediction scenarios. It establishes a lower bound on expected regret for learning algorithms and shows that perfect alignment can significantly reduce this regret, improving learning efficiency. This research is crucial for practitioners as it highlights the importance of designing AI systems that effectively communicate confidence levels to enhance decision-making in high-stakes environments.