Fixed Points and Stochastic Meritocracies: A Long-Term Perspective

Abstract: We study group fairness in the context of feedback loops induced by meritocratic selection into programs that themselves confer additional advantage, like college admissions. We introduce a novel stylized inter-generational model for the setting and analyze it in situations where there are no underlying differences between two populations. We show that, when the benefit of the program (or the harm of not getting into it) is completely symmetric, disparities between the two populations will eventually dissipate. However, the time an accumulated advantage takes to dissipate could be significant, and increases substantially as a function of the relative importance of the program in conveying benefits. We also find that significant disparities can arise due to chance even from completely symmetric initial conditions, especially when populations are small. The introduction of even a slight asymmetry, where the group that has accumulated an advantage becomes slightly preferred, leads to a completely different outcome. In these instances, starting from completely symmetric initial conditions, disparities between groups arise stochastically and then persist over time, yielding a permanent advantage for one group. Our analysis precisely characterizes conditions under which disparities persist or diminish, with a particular focus on the role of the scarcity of available spots in the program and its effectiveness. We also present extensive simulations in a richer model that further support our theoretical results in the simpler, stylized model. Our findings are relevant for the design and implementation of algorithmic fairness interventions in similar selection processes.

Date: May 14th 12:00 PM ET

Register here to attend the colloquium.

Short Bio Juba Ziani: Juba is an Assistant Professor in the School of Industrial and Systems Engineering at Georgia Tech and a recipient of the NSF CAREER Award. His research lies at the intersection of Computer Science, Operations Research, and Economics. He uses tools from learning theory, game theory, and optimization to address technical and societal challenges arising from the rise of AI, ML, and data-driven decision making. He is particularly interested in the economics of data, responsible AI, and strategic behavior in ML settings. Before starting at Georgia Tech, he obtained a PhD in Computer Science from the Computing and Mathematical Science Department at Caltech, where I was advised by Adam Wierman and Katrina Ligett, in 2019. I was a Warren Center Postdoctoral Fellow in the Department of Computer and Information Science at the University of Pennsylvania from 2019 to 2021.

Short Bio Sanmay Das: Sanmay Das is Professor of Computer Science and Associate Director of AI for Social Impact at the Sanghani Center for AI and Data Analytics at the Virginia Tech Institute for Advanced Computing in Alexandria, Virginia. Sanmay is a AAAI Fellow and an ACM Distinguished Member. He is Past Chair of ACM SIGAI, a member of the DARPA ISAT Study Group, and an emeritus member of the board of directors of the International Foundation for Autonomous Agents and Multiagent Systems. He serves as an arXiv moderator and has served in numerous editorial roles, including as associate editor for the ACM Transactions on Economics and Computation, the Journal of Artificial Intelligence Research, and Autonomous Agents and Multiagent Systems. He has served as program co-chair and general co-chair of AAMAS and of the AAAI/ACM Conference on AI, Ethics, and Society, and as Associate Program Chair for IJCAI. He has been recognized with awards for research, teaching, and service, including a National Science Foundation CAREER Award, the Department Chair Award for Outstanding Teaching at Washington University, and the Outstanding Service Award from the Computer Science Department at George Mason University. He has also worked with the US Treasury department on machine learning approaches to credit risk analysis and occasionally consults in the areas of technology and finance.