Aggregating multiple input rankings over a set of candidates to generate a consensus ranking is one of the fundamental ranking problems, having many applications in social choice theory, hiring, college admission, web search, and databases. However, the optimal consensus ranking might be biased against any individual candidate or candidates belonging to certain marginalized communities or groups. This has motivated studies of the rank aggregation problem from the fairness perspective. While finding a consensus ranking, the additional objective is to ensure fair representation of each group in the top positions of the final aggregated ranking. In this talk, we will discuss various algorithms to find such a fair ranking approximately.

Short Bio:

Diptarka is an assistant professor at the National University of Singapore. He did his Ph.D. at the Indian Institute of Technology, Kanpur. Before joining NUS, he spent two years at Charles University, Prague, and then almost a year at Weizmann Institute of Science, Israel, as a post-doctoral fellow. His research interest mostly lies in theoretical computer science, more specifically, algorithms on large data sets, approximation algorithms, sublinear algorithms, string matching algorithms, and graph algorithms. He is a recipient of the best paper award at FOCS 2018 and the Google South & Southeast Asia Research Award 2022.