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Yiling Chen ( firstname.lastname@example.org) is Gordon McKay
Professor of Computer Science at Harvard University,
Arpita Ghosh ( email@example.com) is an associate
professor of information science at Cornell University,
Michael Kearns ( firstname.lastname@example.org) is a professor and
National Center Chair of Computer and Information Science
at the University of Pennsylvania, Philadelphia, PA.
Tim Roughgarden ( email@example.com) is an associate
professor of CS at Stanford University, Stanford, CA.
Jennifer Wortman Vaughan ( firstname.lastname@example.org) is a
senior researcher at Microsoft Research, New York, NY.
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modeling choices, as well as mathematical results suggesting the most important agent characteristics to understand
via experimental research. It will be
important to understand and incorporate relevant research from psychology,
economics, sociology, and other fields.
For example, behavioral economics and
psychology provide insight into how humans respond to incentives.
Generalization. Most of the existing
mathematical work on social computing focuses on a single application.
What does the research on prediction
market design tell us about recommendation systems or citizen science?
Models will have the most potential
for impact if they incorporate reusable components, allowing results to
generalize to many systems. (This is
one motivation for the Crowdsourcing
Compiler discussed earlier.)
A related issue is the lack of consensus and understanding of the “core
social computing problems,” or even
if such a set of core problems exists.
Mathematical theories are typically
developed with one or more such core
problems in mind.
Such problems should capture challenges that span a wide range of applications and be robust to small changes
in the applications to be sure that they
are capturing something “real.” Clearly, the identification of such problems
requires a dialog between practitioners
building real systems and theoreticians
to identify the most pressing problems
requiring mathematical study.
Transparency, interpretability, and
ethical implications. One final challenge to overcome is the potential need
to make social computing algorithms
and models transparent and interpretable to the users of social computing
systems. Users are becoming increasingly sophisticated and are aware the
algorithms employed online impact
both their day-to-day user experience
and their privacy. When faced with the
output of an algorithm, many will question where this output came from and
why. It is already difficult to explain to
users why complex probabilistic algorithms and models produce the results they do, and this will only become
more difficult as algorithms integrate
human behavior to a larger extent.
The issue of algorithmic transpar-
ency is often tied to ethical concerns
such as discrimination and fairness.
Examining and avoiding the unintend-
ed consequences of opaque decisions
made by algorithms is a topic that has
been gaining interest in the machine
learning and big data communities.j
Such concerns will undoubtedly need
to be addressed in the context of social
computing as well.
Acknowledgments. We thank the
participants of the Visioning Workshop on Theoretical Foundations for
Social Computing for their contributions. We also thank Ashish Goel,
Vince Conitzer, David McDonald, David Parkes, and Ariel Procaccia for their
j For example, see the series of recent workshops
on Fairness, Accountability, and Transparency
in Machine Learning ( http://www.fatml.org/).
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