June 13, 2024
Paper draws attention to structural concerns in the debate about equal opportunity and algorithmic fairness
LIDS researchers Shomik Jain, Vinith Suriyakumar, and Ashia Wilson received the best paper award at ACM FAcct 2024! The paper, “Algorithmic Pluralism: A Structural Approach To Equal Opportunity,” highlights several structural concerns in the debate about equal opportunity and algorithmic fairness. In particular, it raises the urgent priority of alleviating severe bottlenecks caused by AI decisions that may systemically exclude certain individuals from opportunities of great consequence. The paper also discusses several ways to bring about pluralism in systems of algorithmic decision-making that can help promote equal opportunity in a systemic way. The team was honored on June 4, 2024, at ACM FAccT in Rio de Janeiro, Brazil.
Ashia Wilson joined the Department of Electrical Engineering and Computer Science as an Assistant Professor in January. Wilson received her PhD in Statistics from the University of California, Berkeley, and her BA in Applied Mathematics from Harvard. Her research centers upon optimization, algorithmic decision making, dynamical systems, and fairness within large scale machine learning. A National Science Foundation Graduate Research Fellow, Wilson has received the NeurIPS ’17 Spotlight Paper Award for The Marginal Value of Adaptive Methods in Machine Learning, and has performed research with Microsoft and Google AI. Her papers have been published in the Proceedings of the National Academy of Science, in Advances in Neural Information Processing Systems, and in the International Conference of Machine Learning, among others. Additionally, she has served as a reviewer for NeurIPS and the Journal of Machine Learning.
Shomik Jain is a PhD student in the Institute for Data, Systems, and Society, advised by Ashia Wilson. His research interests are in randomization for fairness, as well as in robust evaluations of algorithmic systems. He’s also an intern at the National Institute for Standards and Technology, working under the U.S. AI Safety Institute.
Vinith Suriyakumar is a fourth year PhD student at MIT EECS where he is advised by Ashia Wilson and Marzyeh Ghassemi. His research focuses on the intersection of machine learning, statistical inference, and society. He is interested in building theoretically principled algorithms to address security and safety concerns around the use of generative models. He is currently exploring issues surrounding copyright and attribution, unlearning, and backdoors. Additionally, he studies the role of race in medicine and how to use statistical tools to help address health inequities in maternal health.
Kathleen Creel is an assistant professor of philosophy and computer science at Northeastern University, holding joint appointments in the College of Social Sciences and Humanities and Khoury College of Computer Sciences.
ACM FAccT is an interdisciplinary conference dedicated to bringing together a diverse community of scholars from computer science, law, social sciences, and humanities to investigate and tackle issues in the emerging area of Algorithmic systems and fairness. Research challenges include technological solutions regarding potential bias and the question of whether decisions should be outsourced to data- and code-driven computing systems. ACM FAcct seeks to evaluate technical solutions with respect to existing problems, reflecting upon their benefits and risks; to address pivotal questions about economic incentive structures, perverse implications, distribution of power, and redistribution of welfare; and to ground research on fairness, accountability, and transparency in existing legal requirements.
Read the paper.