Date:
Location:
"Computational Social Science: 10 Years Later"
Speaker:
Karen Huang, PhD Candidate, Organizational Behavior, Harvard University; Research Fellow, Program on Science, Technology and Society.
Moderator:
Samuel Weiss Evans, Science, Technology & Society Fellow, Harvard Kennedy School.
Chair:
Sheila Jasanoff, Faculty Associate. Pforzheimer Professor of Science and Technology Studies, Harvard Kennedy School.
Co-sponsored by the Graduate School of Arts and Sciences and the School of Engineering and Applied Sciences.
Contact:
STS Program
sts@hks.harvard.edu
Lunch is provided if you RSVP. via our online form before Thursday afternoon, September 19th:
https://docs.google.com/forms/d/e/1FAIpQLSd7VGUkAvTU655Dub2FTGSNMjpVs6f8Qbu0kpmXh6oz11MgFw/viewform
Abstract:
Computational social science has emerged as a research area built around modern artificial intelligence and machine learning technologies dedicated to analyzing “Big Data” in order to understand patterns of individual and collective behaviors. Since its performative announcement a decade ago, computational social science has expanded throughout the social sciences and industry, but has also encountered controversies that call into question its development. How has computational social science been constructed as a normalized way of knowing and shaping the social world? How do its epistemic assumptions and practices become resilient despite public controversy? The current public focus on issues such as informed consent and data privacy have occluded the computational methods and tools operating behind the scenes. The way that computational social science sees the social world is co-produced with the way in which its actors wish to solve problems aligned with particular conceptions of social progress. Oriented around the development of tools for analyzing “Big Data,” computational social science shapes and is shaped by interests in predicting and influencing human behavior at scale. The paper concludes by discussing normative implications and possible paths forward for computational social science.
Bio:
Karen Huang is a Ph.D. Candidate in Organizational Behavior with a secondary field in STS at Harvard University. She is a Fellow in the STS Program at Harvard, 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. Her research in STS investigates the expansion of computer science expertise into the social sciences and conceptions of progress. In her current research, she looks at the development of micro-targeting practices in social data science -- how they shape and are shaped by "Big Data" and the tech industry -- as well as the politics of framing controversies in machine learning as issues of "fairness" and "privacy". She is particularly interested in why particular ethical frameworks become privileged as the dominant discourse. In her research, Karen draws from her training and background in several disciplinary approaches to ethics. Before starting her doctoral studies, Karen studied phenomenology at Bard College Berlin. She holds a Bachelor’s Degree in Ethics, Politics & Economics (specializing in political philosophy) from Yale University, and a Master’s in Psychology from Harvard University.
A complete list of STS Circle at Harvard events can be found on
http://sts.hks.harvard.edu/events/sts_circle/
