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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.


News

News
Dec 14, 2018

Get to Know Berkman Klein Fellow Lily Hu

a spotlight on one of our 2018-2019 BKC Fellows

How are algorithms distributing power between people? More


Community

Communications of the ACM

Embedded EthiCS: Integrating Ethics Across CS Education

Embedded EthiCS employs a distributed pedagogy that makes ethical reasoning an integral component of courses throughout the standard computer science curriculum.

Aug 6, 2019
Medium

What Do We Owe to the Internet’s “First Responders?”

Experts share perspectives on the ethics and legality of how social platforms moderate content

Until AI catches up, tens of thousands of human content moderators all around the world will continue to ingest thousands of posts of potentially toxic posts on social media,…

Apr 17, 2019
Harvard Magazine

Ethics and the dawn of decision-making machines

Even a thoughtfully designed algorithm must make decisions based on inputs from a flawed, imperfect, unpredictable, idiosyncratic real world

Dec 17, 2018

Events

Event
Apr 29, 2019 @ 12:00 PM

Having our cake and eating it too

How to develop AI competitively without falling victim to collective action problems

VIDEO: Could competition to develop new AI systems cause companies to cut corners ensuring their systems are safe and beneficial? More

Apr 12, 2019 @ 3:00 PM

The Cleaners

Film Screening and Panel Discussion

Told in the sinister style of a neon, cyberpunk thriller, The Cleaners charts social media’s evolution from a shared vision of a global village to a dangerous web of fake news,… More