[Colloquium] TOMORROW 1/31 Emily Diana (UPenn) Addressing Algorithmic Bias and Disclosiveness: Minimax Fairness and Multiaccurate Proxies

[Holly Santos]

Department of Computer Science Seminar

Emily Diana
PhD Candidate, Department of Statistics and Data Science
Wharton School, University of Pennsylvania

Tuesday, January 31st
2:00pm - 3:00pm
In Person: John Crerar Library 390

Zoom:
https://uchicagogroup.zoom.us/j/94653330201?pwd=K3B0N1ZMN2FwbS8wZEtja0wxTlpBUT09

Meeting ID: 946 5333 0201
Passcode: 114740

Title: Addressing Algorithmic Bias and Disclosiveness: Minimax Fairness and Multiaccurate Proxies

Abstract:
While data science enables rapid societal advancement, deferring decisions to machines does not automatically avoid egregious equity or privacy violations. Without safeguards in the scientific process --- from data collection to algorithm design to model deployment --- machine learning models can easily inherit or amplify existing biases and vulnerabilities present in society. My research focuses on explicitly encoding algorithms with ethical norms and constructing frameworks ensuring that statistics and machine learning methods are deployed in a socially responsible manner. In particular, I develop theoretically rigorous and empirically verified algorithms to mitigate automated bias and protect individual privacy.
I will highlight this through two main contributions:
(1) A new oracle-efficient and convergent algorithm to provably achieve minimax group fairness -- fairness measured by worst-case outcomes across groups -- in general settings (“Minimax Group Fairness: Algorithms and Experiments,” https://dl.acm.org/doi/10.1145/3461702.3462523).
(2) A framework for producing a sensitive attribute proxy that allows one to train a fair model even when the original sensitive features are not available (“Multiaccurate Proxies for Downstream Fairness,” https://dl.acm.org/doi/10.1145/3531146.3533180).
Bio:
Emily Diana is a Ph.D. candidate in Statistics and Data Science at the Wharton School of the University of Pennsylvania, where her research focuses on the intersection of ethical algorithm design and socially responsible machine learning. She holds a B.A. in Applied Mathematics from Yale College and an M.S. in Statistics from Stanford University. Before graduate school, she spent two years as a software developer at Lawrence Livermore National Laboratory (LLNL), working on high-performance computing and government finite element physics simulation codes. She is honored to be the 2022 recipient of Wharton’s J. Parker Memorial Bursk Prize for Excellence in Research and to have been recognized as both a 2022 Future Leader in Data Science by the Michigan Institute for Data Science and a 2021 Rising Star in EECS by MIT. More information can be found on her website at www.emilydiana.com<http://www.emilydiana.com/>.

[EmilyDiana.jpg]

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Holly Santos
Executive Assistant to Michael J. Franklin, Chairman
Department of Computer Science
The University of Chicago
5730 S Ellis Ave-217   Chicago, IL 60637
P: 773-834-8977
hsantos at uchicago.edu<mailto:hsantos at uchicago.edu>

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