Mon, Oct 03, 2022 @ 03:00 PM - 04:30 PM
PhD Candidate: Umang Gupta
Title: Controlling Information for Fairness and Privacy in Machine Learning
Committee: Greg Ver Steeg, Paul Thompson, Bistra Dilkina, Kristina Lerman, Fred Morstatter.
With the increasing ubiquity of machine learning models in everyday life, a critical issue occurs when these models capture unintended information. This leads to unintended biases and memorization of training data, resulting in unfair outcomes and risking privacy. These phenomena are especially troublesome in applications where data privacy needs to be upheld, such as medical imaging, or where unfairness can lead to disparate outcomes, such as hiring decisions. To this end, we study this underlying problem of capturing unintended information in various domains. Specifically, we discuss ways to ensure fairness in decision-making by learning fair data representations and controlling unfair language generation by correctly modulating information in neural networks. Finally, we demonstrate that releasing neuroimaging models can reveal private information about the individuals participating in the training set and discuss ongoing work on learning with privacy.
Audiences: Everyone Is Invited
Contact: Lizsl De Leon