Human Decisions and Machine Predictions
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An increasing number of domains are providing us with detailed trace data on human decisions, often made by experts with deep experience in the subject matter. This provides an opportunity to use machine-learning prediction algorithms to ask several families of questions --- not only about the extent to which algorithms can outperform expert-level human decision-making in specific domains, but also whether we can use algorithms to analyze the nature of the errors made by human experts, to predict which instances will be hardest for these experts, and to explore some of the ways in which prediction algorithms can serve as supplements to human decision-making in different applications. In this talk, I'll explore this theme by drawing on a line of recent projects; all are joint with Sendhil Mullainathan, and include collaborations with Ashton Anderson, Himabindu Lakkaraju, Jure Leskovec, Annie Liang, and Jens Ludwig.
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