Fairness in Submodular Maximization over a Matroid Constraint
This work addresses fairness in a fundamental machine learning problem for decision-making with sensitive data, but it is incremental as it extends prior work from cardinality to matroid constraints.
The paper tackles the problem of ensuring fairness in submodular maximization under a matroid constraint, which is crucial for applications involving sensitive attributes like gender or race, and proposes algorithms and impossibility results that provide trade-offs between quality, fairness, and generality.
Submodular maximization over a matroid constraint is a fundamental problem with various applications in machine learning. Some of these applications involve decision-making over datapoints with sensitive attributes such as gender or race. In such settings, it is crucial to guarantee that the selected solution is fairly distributed with respect to this attribute. Recently, fairness has been investigated in submodular maximization under a cardinality constraint in both the streaming and offline settings, however the more general problem with matroid constraint has only been considered in the streaming setting and only for monotone objectives. This work fills this gap. We propose various algorithms and impossibility results offering different trade-offs between quality, fairness, and generality.