Title: Understanding and Mitigating Bias in Algorithms, Data and Society
Date: Tuesday, July 25th, 2023
Time: 10:00 am - 12:00 pm, EST
Location (in-person): Coda C1215
Location (remote):
Zoom link - https://gatech.zoom.us/j/94142589173?pwd=TmVvM0RDQ0kvVCtDYUlFWGpEdVA0Zz09
Meeting ID: 941 4258 9173
Passcode: 948416
Zhanzhan Zhao
Machine Learning Ph.D. Student
School of Computer Science
Georgia Institute of Technology
Committee
Dr. Dana Randall (Advisor) - School of Computer Science, Georgia Institute of Technology
Dr. Constantine Dovrolis - School of Computer Science, Georgia Institute of Technology
Dr. Gregory Joseph Herschlag - Department of Mathematics, Duke University
Dr. Srijan Kumar - School of Computational Science and Engineering, Georgia Institute of Technology
Dr. Will Perkins - School of Computer Science, Georgia Institute of Technology
Dr. Catherine Ross - School of City & Regional Planning, Georgia Institute of Technology
Abstract
Societal biases are systemic prejudices embedded in individuals’ and institutions’ collective beliefs, attitudes, norms, and behaviors, including racial biases, ideological biases, and partisan biases. While computer scientists have made significant strides in quantifying patterns of bias and developing mitigation strategies for some computer algorithms and internet applications, exploring the root causes of most societal biases remains a challenge. Due to the complex nature and unpredictability of emergent societal behaviors and the limited data availability, many powerful algorithms and data analysis tools face inherent limitations.
This thesis develops new algorithms and data analytical tools for identifying and addressing societal biases. We take a multi-lens approach, combining algorithmic modeling, data analysis, and qualitative theories to gain more comprehensive insights into societal biases along three specific themes: the persistent residential segregation arising from racial homophily, ideological polarization exacerbated by reinforcements of beliefs, and non-responsiveness in political elections boosted by partisan redistricting policies. By integrating these disparate research methods, we aim to develop a deeper understanding of these equity-related social problems and propose system-level mitigation interventions.
First, using tools from probability and theoretical computer science, we rigorously examine the possible causal mechanisms behind residential segregation through Markov chain analysis. We prove how the careful placement of incentives, such as urban amenities, can either worsen or alleviate segregation, supported by machine learning analysis of U.S. cities. Second, we explore how personalized recommender algorithms can contribute to extreme polarization through agent-based modeling. Our simulations highlight the effectiveness of novel local recommendation designs in reversing polarization, even in the presence of extreme influencers and social pressure. We emphasize the importance of tailored interventions that leverage diversity to foster inclusive dialogue and bridge ideological divides. Finally, we also develop a fair evaluation method for biases in public policies, proposing a novel computational protocol with improved efficiency. Our study of Georgia's districting plans uncovers their inadequate responsiveness to changing voter opinions, emphasizing the instrumental role of algorithms and data analysis in auditing and bringing clarity to public policies.