Fairness in an Algorithmic World

Siddharth’s recent work considers fairness from a computation lens.

For example, one of his recent works shows that one can compute outcomes, in economic systems, which are efficient and fair at the same time, i.e., the seeming incompatible properties of efficiency and fairness can be achieved together.

Given that fairness is a fundamental consideration in many resource-allocation problems, these results can potentially influence allocation policies in practical settings. Siddharth is also interested in developing fairness guarantees in machine-learning contexts such as clustering and classification.



Siddharth Barman, Sanath Kumar KrishnamurthyRohit Vaish: Finding Fair and Efficient Allocations. Economics and Computation, EC 2018: 557-574


Eshwar Ram Arunachaleswaran, Siddharth Barman, Nidhi Rathi: Fully Polynomial-Time Approximation Schemes for Fair Rent Division. SODA 2019: 1994-2013


Siddharth Barman, Nidhi Rathi: Fair Cake Division Under Monotone Likelihood Ratios. Economics and Computation, EC 2020: 401-437


Faculty: Siddharth Barman, CSA
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