演講者簡介 : Professor Liang received her Ph.D. in Economic from Harvard University in 2016. She is currently an Assistant Professor at Northwestern University. Her research is in economic theory (in particular, learning and information), and the application of machine learning methods for model building and evaluation.
演講摘要 : Markets for lending and insurance incentivize good behavior by forecasting risk on the basis of past outcomes. As “big data” expands the set of covariates used to predict risk, how will these incentives change? We show that “attribute” data which is in-formative about consumer quality tends to decrease effort, while “circumstance” data which predicts idiosyncratic shocks to outcomes tends to increase it. When covariates are independent, this effect is uniform across all consumers. Under more general forms of correlation, this effect continues to hold on average, but observation of a new co-variate may lead to disparate impact—increasing effort for some consumer groups and decreasing it for others. A regulator can improve social welfare by restricting the use of either attribute or circumstance data, and by limiting the use of covariates with substantial disparate impact.
