Robust Optimization of Fair Machine Learning Objectives

Core Concepts

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.

Abstract

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.

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Key Insights Distilled From

Algorithms and Analysis for Optimizing Robust Objectives in Fair Machine Learning

by Cyrus Cousin... at arxiv.org 04-11-2024

https://arxiv.org/pdf/2404.06703.pdf

Deeper Inquiries

How can the Gini power-mean class be axiomatically characterized

The Gini power-mean class can be axiomatically characterized by considering a weighted power-mean family of aggregator functions. This class combines the piecewise nature of the Gini family with the continuously differentiable nonlinear nature of the power-mean family. The Gini power-mean class generalizes both the unweighted power-mean and Gini families, encompassing a spectrum of fairness concepts between utilitarian and egalitarian welfare or malfare. The class is defined by sorting (dis)utilities, assigning weights in ascending or descending order, and taking a weighted power-mean. A key aspect of this characterization is the combination of the Gini axioms and power-mean axioms, resulting in a unique class that falls between the extremes of utilitarian and egalitarian welfare.

What are the implications of this work for the design of fair and robust machine learning systems in practice

The implications of this work for the design of fair and robust machine learning systems in practice are significant. By deriving robust variants of fair objectives and exploring the mathematical and philosophical connections between fairness, robustness, and uncertainty, this research provides a framework for optimizing machine learning algorithms with fairness considerations. The ability to efficiently optimize robust fair objectives under mild conditions using standard maximin optimization techniques opens up new possibilities for developing machine learning models that prioritize fairness and robustness. This can lead to the creation of more ethical and reliable machine learning systems that consider the impact on all individuals impartially, aligning with principles of fairness and justice.

How might the connections between fairness, robustness, and uncertainty explored in this paper inform broader discussions around the ethics and philosophy of artificial intelligence

The connections between fairness, robustness, and uncertainty explored in this paper have profound implications for the broader discussions around the ethics and philosophy of artificial intelligence. By linking concepts such as Rawlsian ethics, adversarial optimization, and welfare functions, the research sheds light on the intricate relationship between fairness and robustness in machine learning systems. These insights can inform ethical considerations in AI development, highlighting the importance of designing algorithms that are not only accurate and efficient but also fair and robust. The philosophical implications of considering fairness from behind a veil of ignorance and incorporating robustness against uncertainty can lead to more transparent and accountable AI systems that prioritize ethical decision-making and societal well-being.