Program on Science, Technology and Society at Harvard|
Karen Huang is a Ph.D. Candidate in Organizational Behavior with a secondary field in STS at Harvard University. She is also a Fellow in the STS Program at Harvard Kennedy School, and a Fellow at the Berkman Klein Center for Internet & Society. Karen works in several interdisciplinary research streams, drawing from STS, ethics, psychology, and political philosophy.
As a Fellow in the STS Program, she will be investigating the development of computer science and engineering approaches to understanding the social world and the human subject, and to shaping definitions of social progress. Karen is also doing research on the politics of framing ethical concerns as “fairness” and “privacy” in data science and machine learning. She is particularly interested in why particular ethical frameworks become privileged as the dominant discourse.
In addition, Karen often collaborates with computer scientists to investigate conceptualizations of artificial intelligence and fairness in machine learning. For example, one project shows that machine learning researchers tend to favor definitions of AI that emphasize technical functionality while policy-makers favor definitions of AI that emphasize comparison to human behavior and artificial general intelligence.
In her research, Karen draws from her training and background in several disciplinary approaches to ethics, including from philosophy, psychology, and STS. Before starting her doctoral studies, Karen studied phenomenology at Bard College Berlin. She holds a B.A. in Ethics, Politics & Economics (specializing in political philosophy) from Yale University, and a M.A. in Psychology from Harvard University. In addition, Karen maintains an artistic practice as a form of ethnographic research and critical inquiry, working within dance, experimental film, and installation.
Note: The above information concerns a past fellow at the Program on Science, Technology, and Society at the Harvard Kennedy School. It does not constituent evidence of current enrollment. The information may be out of date. To update their information, past fellows should e-mail the site administrator.