Human-AI Collaboration
Overview
Human-AI collaboration studies how people and AI share tasks, exchange information, and adjust roles in settings such as clinical care, software engineering, education, science, and creative work. Approaches include task allocation and deferral, calibrated advice and ensembles, and interactive or multi-agent workflows with planning, shared context, feedback, and human checkpoints. Results depend not just on model accuracy but also on timing, user expertise, trust, task structure, and whether people can inspect and correct intermediate work. A central tension is that assistance can improve immediate outcomes while weakening independent skill, ownership, or accountability; evaluation increasingly considers interaction processes, reliance, learning, and safety alongside task success.
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