Human-AI Decision Making
Overview
The field examines how people and AI can combine judgment, divide tasks, or transfer decision authority, especially when decisions are uncertain or consequential. Common approaches include calibrated advice, selective prediction and deferral, adaptive routing and ensembles, preference elicitation, and explanations designed to support human review. Researchers test these methods in behavioral experiments and applied settings such as clinical diagnosis, finance, negotiation, and public services, measuring both decision outcomes and interaction behavior. A central tension is that AI assistance can improve performance yet also encourage overreliance, weaken human skills, or shift influence and responsibility; gains depend on task context, complementary strengths, and whether people can detect when the AI is wrong.
Latest papers
175 papers22 papers in the last four weeks, up 5% on the four weeks before. 0.1% of all new papers.