Lily Hu is a Ph.D. candidate in Applied Mathematics at Harvard University who splits her time between working in the fields of algorithmic fairness and machine learning and thinking and writing about the philosophy and politics of artificial intelligence. At the Berkman Klein Center, she will study the role of algorithmic systems as resource distribution mechanisms with a focus on how their design, adoption, and deployment bear on matters of distributive justice.
Her contributions to the fair machine learning literature stand at the intersection of mathematics, computer science, and economics. Her research in this area has studied the long-term effects of statistical discrimination in the labor market, welfare and distributive impacts of fair machine learning, and the interplay between strategic machine classification and social stratification. In her more critically-oriented work on algorithmic systems, she has written on the (mis)use of causal inference and counterfactuals in reasoning about fairness in machine learning and artificial intelligence's participation in reawakening essentialist conceptions of socially-constructed identity attributes.
