Distinguished Lecture on May 1st: Cynthia Dwork
Fairness Through Awareness
Cynthia Dwork, Microsoft, SVC
Wednesday, May 1, 2013 in the Hariri Institute at 11am
“Why was I not shown this advertisement? Why was my loan application
denied? Why was I rejected from this university?”
This talk will address fairness in classification, where the goal is
to prevent discrimination against protected population subgroups in
classification systems while simultaneously preserving utility for the
party carrying out the classification, for example, the advertiser,
bank, or admissions committee. We argue that a classification is fair
only when individuals who are similar with respect to the
classification task at hand are treated similarly, and this in turn
requires understanding of sub cultures of the population. Similarity
metrics are applied in many contexts, but these are often hidden. Our
work explicitly exposes the metric, opening it to public debate.
(Joint work with Moritz Hardt, Toniann Pitassi, Omer Reingold, and
Richard Zemel.)
Our approach provides a (theoretical) method by which an on-line
advertising network can prevent discrimination against protected
groups, even when the advertisers are unknown and untrusted. We
briefly discuss the role of fairness in consumer objections to
behavioral targeting and explain how traditional notions of privacy
miss the mark and fail to address these. (Joint work with Deirdre
Mulligan.)
Finally, we discuss a machine learning instantiation of our approach,
in which the distance metric need not be given but can instead be
learned. (Joint work with Toniann Pitassi, Yu Wu, and Richard Zemel.)