The project contains the code of our project Maximizing Coverage While Ensuring Fairness: A Tale of Conflicting Objectives.
Ensuring fairness in computational problems has emerged as a key topic during recent years, buoyed by considerations for equitable resource distributions and social justice. It is possible to incorporate fairness in computational problems from several perspectives, such as using optimization, game-theoretic or machine learning frameworks. In this paper we address the problem of incorporation of fairness from a combinatorial optimization perspective. We formulate a combinatorial optimization framework, suitable for analysis by researchers in approximation algorithms and related areas, that incorporates fairness in maximum coverage problems as an interplay between two conflicting objectives. Fairness is imposed in coverage by using coloring constraints that minimizes the discrepancies between number of elements of different colors covered by selected sets; this is in contrast to the usual discrepancy minimization problems studied extensively in the literature where (usually two) colors are not given a priori but need to be selected to minimize the maximum color discrepancy of each individual set. Our main results are a set of randomized and deterministic approximation algorithms that attempts to simultaneously approximate both fairness and coverage in this framework.
[1] Abolfazl Asudeh, Tanya Berger-Wolf, Bhaskar DasGupta, Anastasios Sidiropoulos. Maximizing coverage while ensuring fairness: a tale of conflicting objective. Algorithmica, 2022, Springer.
This project is licensed under the MIT License - see the LICENSE.md file for details
