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Amit Deshpande studied mathematics and theoretical computer science at Chennai Mathematical Institute (CMI) and Massachusetts Institute of Technology (MIT). He joined Microsoft Research India after graduation and continues to work there. His current interests lie in responsible AI, more specifically, in fixing data bias and plumbing AI/ML pipelines.

Abstract: AI/ML systems routinely classify and rank data, products, media, even people. Allowing randomized or stochastic decisions is even more contentious. On the one hand, we toss a coin to break ties fairly all the time. On the other hand, even a fair coin toss discriminates between similar individuals for no valid reason. In this talk, we will debate some provable benefits and drawbacks of randomization in fair classification and ranking. For example, can a randomized fair classifier give better accuracy than a deterministic fair classifier? Is a randomized fair classifier more robust to data shift than a deterministic fair classifier? Can a randomized ranker maximize ranking utility while allowing fair representation of two demographic groups in the top ranks, not just in expectation but in every output?