Fairness in AI and in Algorithms


With the increasing role played by AI and algorithms in our lives, it is well-recognized now that fairness should be a key aspect of such AI systems, to avoid automated decision-making that may (inadvertently) be biased. After surveying some of the approaches considered in this general area, we will discuss some of our work on fairness – particularly in unsupervised learning.

About the speaker

Aravind Srinivasan Aravind Srinivasan is a Distinguished University Professor and a Professor with the Department of Computer Science and the Institute for Advanced Computer Studies at the University of Maryland, College Park, USA. He received his undergraduate degree from the Indian Institute of Technology, Madras, and his Ph.D. from Cornell University. His research interests include randomized algorithms, algorithms and models in AI, E-commerce, public health, digital health, networking, social networks, and combinatorial optimization.

He is Editor-in-Chief of the ACM Transactions on Algorithms, and an Editor for Theory of Computing (Managing Editor, 2006-2019) as well as for the Journal of the IISc. He is an elected Fellow of six professional societies: ACM, AAAS, IEEE, AMS, EATCS, and SIAM. He was elected a Member of Academia Europaea, the Academy of Europe, in 2018. He received a Distinguished Alumnus Award from his alma mater IIT Madras. He also received the Distinguished Faculty Award from the Board of Visitors of the College of Computing, Mathematical, and Natural Sciences (University of Maryland). He is a recipient of the Dijkstra Prize and the Danny Lewin Award.