[AI Seminar] Talk by Omer Reingold on March 29

[Ariel Procaccia]

Of possible interest:

*ECE Seminar: *Calibration for the (computationally-identifiable) masses

*Starts at: *March 29, 2018 4:30 PM

*Ends at: *6:00 PM

*Location: *DH A302

*Speaker: *Dr. Omer Reingold

*Affiliation: *Stanford University

*Refreshments provided: *Yes

Link to Abstract
<http://www.ece.cmu.edu/news/calendar/2018/03/ece-grad-seminar-reingold.pdf>

  iCalendar
<http://www.ece.cmu.edu/news/calendar/2018/03/ece-grad-seminar-reingold.ics>

*Details:*

Abstract:
As algorithms increasingly inform and influence decisions made about
individuals, it becomes increasingly important to address concerns that
these algorithms might be discriminatory. The output of an algorithm can be
discriminatory for many reasons, most notably: (1) the data used to train
the algorithm might be biased (in various ways) to favor certain
populations over others; (2) the analysis of this training data might
inadvertently or maliciously introduce biases that are not borne out in the
data. This work focuses on the latter concern.

We develop and study multicalbration as a new measure of algorithmic
fairness that aims to mitigate concerns about discrimination that is
introduced in the process of learning a predictor from data.
Multicalibration guarantees accurate (calibrated) predictions for every
subpopulation that can be identified within a specified class of
computations. We think of the class as being quite rich, in particular it
can contain many and overlapping subgroups of a protected group.

We show that in many settings this strong notion of protection from
discrimination is both attainable and aligned with the goal of obtaining
accurate predictions. Along the way, we present new algorithms for learning
a multicalibrated predictor, study the computational complexity of this
task, and draw new connections to computational learning models such as
agnostic learning.

Joint work with Ursula Hebert-Johnson, Michael P. Kim and Guy Rothblum

Bio:
Omer Reingold is a Professor of Computer Science at Stanford University.
Past positions include Samsung Research America, the Weizmann Institute of
Science, Microsoft Research, the Institute for Advanced Study in Princeton,
NJ and AT&T Labs. His research is in the Foundations of Computer Science
and most notably in Computational Complexity and the Foundations of
Cryptography with emphasis on randomness, derandomization and explicit
combinatorial constructions. He has a keen interest in the societal impact
of computation. He is an ACM Fellow and among his distinctions are the 2005
Grace Murray Hopper Award and the 2009 Gödel Prize.
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