Concepts de base
The core message of this paper is to derive robust variants of fair objectives, such as utilitarian, Gini, and power-mean welfare concepts, by constructing a hierarchy of Rawlsian games where a Dæmon creates a world and an adversarial Angel places the Dæmon within it. These robust fair objectives can be efficiently optimized under mild conditions.
Résumé
The paper extends ideas and objectives in welfare-centric fair machine learning and optimization. It derives robust variants of fair objectives and explores the mathematical and philosophical connections between robustness and fairness.
The key contributions are:
Providing philosophical insight into a large class of welfare and malfare functions by deriving them as robust utilitarian welfare in a Rawlsian game. The paper also shows that some welfare (malfare) concepts arise from concave utility (convex disutility) transforms.
Arguing that utilitarian and egalitarian welfare/malfare are two ends of a spectrum, and deriving a novel class of welfare (malfare) functions, the Gini power-mean class, that falls between these extremes.
Leveraging the connections between fairness, robustness, and robust fairness, the paper shows that the robust fair objectives can be efficiently optimized in various allocation and machine learning applications.
The paper first describes John Rawls' original position argument and several generalizations that give rise to various robust fairness concepts. It then shows that these robust fair objectives yield probabilistic or adversarial guarantees in terms of their non-robust counterparts. Finally, the paper demonstrates efficient optimization of the fair and robust fair objectives in different settings.