[AI Seminar] Update: Talk by Omer Reingold on March 29

[Ariel Procaccia]

Reminder: this talk is on Thursday.


> On Tue, Mar 13, 2018 at 4:04 PM, Ariel Procaccia <arielpro at cs.cmu.edu>
> wrote:
>
>> 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: * Hamerschlag Hall 1107
>>
>> *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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