[Colloquium] TODAY, 2pm: Data Science/CS Candidate: Chara Podimata (Harvard)

[Rob Mitchum]

*Data Science Institute/Computer Science Candidate Seminar*

*Chara Podimata*
*Ph.D. Student*
*Harvard University*

*Thursday, February 10th*
*2:00 p.m. - 3:00 p.m.*
*In-Person: John Crerar Library, Room 390*
*Remote: Live Stream <http://live.cs.uchicago.edu/charapodimata/> or Zoom
<https://uchicago.zoom.us/j/99030300024?pwd=KytQUTVuemF3T0dRcmJ1WUpWMlZSZz0>
(details
below)*


*Incentive-Aware Machine Learning for Decision Making*

As machine learning algorithms are increasingly being deployed for
consequential decision making (e.g., loan approvals, college admissions,
probation decisions etc.) humans are trying to strategically change the
data they feed to these algorithms in an effort to obtain better decisions
for themselves. If the deployed algorithms do not take these incentives
into account they risk creating policy decisions that are incompatible with
the original policy’s goal.

In this talk, I will give an overview of my work on Incentive-Aware Machine
Learning for Decision Making, which studies the effects of strategic
behavior both to institutions and society as a whole and proposes ways to
robustify machine learning algorithms to strategic individuals. I will
first explain the goals of the different stakeholders (institution,
individual, society) in these settings in a unified way and show the
various settings I have worked on that belong in the incentive-aware
machine learning area such as incentive-compatible algorithms for linear
regression and online prediction with expert advice, strategic
classification, learning in auctions, and dynamic pricing. I will conclude
by looking at the problem from a societal lens and discuss the tension that
arises between having decision-making algorithms that are fully transparent
and incentive-aware.

*Bio*: Chara <https://www.charapodimata.com/> is a final year PhD student
at Harvard, where she is advised by Yiling Chen. Her research is generously
supported by a Microsoft Dissertation Grant and a Siebel Scholarship.
During her PhD, she interned twice for MSR NYC (mentored by Jennifer
Wortman Vaughan and Aleksandrs Slivkins) and once for Google Research NYC
(mentored by Renato Paes Leme). She has given tutorials related to
strategic learning at EC20 and FAccT21. Outside of research, she spends her
time adventuring with her pup, Terra.

*Host*: Raul Castro Fernandez

*Zoom Info:*
https://uchicago.zoom.us/j/99030300024?pwd=KytQUTVuemF3T0dRcmJ1WUpWMlZSZz0
Meeting ID: 990 3030 0024
Passcode: ds2022



-- 
*Rob Mitchum*

*Associate Director of Communications for Data Science and Computing*
*University of Chicago*
*rmitchum at uchicago.edu <rmitchum at ci.uchicago.edu>*
*773-484-9890*
-------------- next part --------------
An HTML attachment was scrubbed...
URL: <http://mailman.cs.uchicago.edu/pipermail/colloquium/attachments/20220210/ebf29a14/attachment.html>