Computational Social Science: 10 Years Later

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.