Human-AI Complementarity for Decision Making

12 units · Graduate

Spring: 12 units Humans and AIs bring distinct strengths to decision-making in uncertain, dynamic, and complex environments. How can we design human-AI decision making systems that draw on the strengths of humans and AI technologies to remedy the limitations and weaknesses of each one in isolation and improve the quality of the resulting decisions? This course will explore the emerging science of human-AI decision-making, focusing on how humans and AIs can complement each other. The course will teach students to identify the conditions and criteria for human-AI complementarity, determine the major research gaps in prior research, and put forward an interdisciplinary research agenda to mitigate and address these gaps. The students in the course are expected to define and write down a concrete research proposal addressing one of the core topics of the course. Prerequisites: 10-701 Min. grade C or 15-281 Min. grade C or 07-180 Min. grade C or 07-280 Min. grade C or 10-315 Min. grade C or 10-601 Min. grade C or 10-715 Min. grade C or 10-301 Min. grade C