[Colloquium] TODAY, 3PM: Data Science/CS Candidate Talk - YooJung Choi (UCLA)

[Rob Mitchum]

*Data Science Institute/Computer Science Candidate Seminar*

*YooJung Choi*
*PhD Student*
*University of California, Los Angeles*

*Wednesday, February 23rd*
*3:00 p.m. - 4:00 p.m.*
*In-Person: John Crerar Library, Room 390*
*Remote: Live Stream <http://live.cs.uchicago.edu/yoojungchoi/> or Zoom
<https://uchicago.zoom.us/s/91854078296?pwd=MEs0aER5ZkxFZlhQL1hXQmZua1VsQT09>
(details
below)*


*Tractable Probabilistic Reasoning for Trustworthy AI*

Automated decision-making systems are increasingly being deployed in areas
with personal and societal impacts, leading to growing interest and need
for trustworthy AI and ML systems–that is, models that are robust,
explainable, fair, and so on. It is important to note that these guarantees
only hold with respect to a certain model of the world, with inherent
uncertainties. In this talk, I will present how probabilistic modeling and
reasoning, by incorporating a distribution, offer a principled way to
handle different kinds of uncertainties when learning and deploying
trustworthy AI systems. For example, when learning classifiers, the labels
in the training data may be biased; I will show that probabilistic
circuits, a class of tractable probabilistic models, can be effective in
enforcing and auditing fairness properties by explicitly modeling a latent
unbiased label. In addition, I will also discuss recent breakthroughs in
tractable inference of more complex queries such as information-theoretic
quantities, to demonstrate the potential of probabilistic reasoning for
trustworthy AI. Finally, I will conclude with my future work towards a
framework to flexibly reason about and enforce trustworthy AI/ML system
behaviors.

*Bio*: YooJung Choi <https://web.cs.ucla.edu/~yjchoi/> is a PhD student in
the Computer Science department at UCLA, advised by Guy Van den Broeck. She
is part of the Statistical and Relational Artificial Intelligence (StarAI)
lab. Her research interests are in tractable probabilistic models,
graphical models, knowledge compilation, and trustworthy AI/ML (robustness,
fairness, explainability, and more).

*Host*: Yuxin Chen

*Zoom Info:*
https://uchicago.zoom.us/s/91854078296?pwd=MEs0aER5ZkxFZlhQL1hXQmZua1VsQT09
Meeting ID: 918 5407 8296
Password: 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*
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