*Presentation will be recorded.
Abstract:
From Plato's Republica to John Rawl's Theory of Justice, every major work on the ethical foundations of human society, has held fairness at its zenith. Today, in the age of algorithms, we are equipped with substantial computational resources, and rigorous data driven decision making processes, to address the fairness concerns better than ever (fairness through computation). Conversely, with algorithms used extensively to make decisions of large societal impact, fairness has also evolved to be an integral requirement of several algorithmic paradigms (fairness in computation).
In this talk, I discuss:
(i) algorithmic solutions to two fundamental fairness problems in algorithmic game theory (fairness through computation).
(ii) efficient algorithms integrating fairness notions from social choice theory, and microeconomics to fairness demanding settings in machine learning like federated learning and fairness aware classification (fairness in computation).
Bio:
Bhaskar Ray Chaudhury is currently a Future Faculty Fellow at the University of Illinois at Urbana Champaign. He received his PhD from Max Planck Institute for Informatics under the supervision of Kurt Mehlhorn and Karl Bringmann. He is broadly interested in operations research, economics and computation, computational social choice theory, and machine learning. His work on computational social choice was recognized by the Exemplary Paper in the Theory Track Award and the Best Paper with a Student Lead Author Award at the 21st ACM conference on Economics and Computation (EC 2020). He is also the recipient of the "Teachers Ranked Excellent by Students" award at UIUC.
