Cardinal objectives serve as intuitive targets in fair machine learning by summarizing utility
(welfare) or disutility (malfare) $ u $ over $ g $ groups. Under standard axioms, all welfare …

In fair machine learning, one source of performance disparities between groups is overfitting
to groups with relatively few training samples. We derive group-specific bounds on the …

The original position or veil of ignorance argument of John Rawls, perhaps the most famous
argument for egalitarianism, states that our concept of fairness, justice, or welfare should be …

As the negative societal consequences of machine learning systems run amok have
become increasingly apparent, fair machine learning methods have seen increased …

In recent years, empirical game-theoretic analysis (EGTA) has emerged as a powerful tool
by which to analyze multiagent systems [2, 22, 23, 28], particularly when only a simulator of …

We study the problem of fairly allocating a set of indivisible goods among agents with
bivalued submodular valuations—each good provides a marginal gain of either a or b (a< b) …

Defining demographic categories for fair learning frequently requires both private
information collection at the individual level and expert judgment in the construction of …

Empirical game-theoretic analysis (EGTA) is primarily focused on learning the equilibria of
simulation-based games. Recent approaches have tackled this problem by learning a …

We study the problem of fairly allocating a set of indivisible goods among agents with {\em
bivalued submodular valuations}--each good provides a marginal gain of either $ a $ or $ b …

We propose a welfare-centric fair reinforcement-learning setting, in which an agent enjoys
vector-valued reward from a set of beneficiaries. Given a welfare function W (·), the task is to …