Now that machine learning algorithms lie at the center of many important resource allocation pipelines, computer scientists have been unwittingly cast as partial social planners. Given this state of aairs, important questions follow. How do leading notions of fairness as dened by computer scientists map onto longer-standing notions of social welfare? In this paper, we present a welfare-based analysis of fair classication regimes. Our main ndings assess the welfare impact of fairness-constrained empirical risk minimization programs on the individuals and groups who are subject to their outputs. We fully characterize the ranges of perturbations to a fairness parameter in a fair Soft Margin SVM problem that yield better, worse, and neutral outcomes in utility for individuals and by extension, groups. Our method of analysis allows for fast and ecient computation of "fairness-to-welfare" solution paths, thereby allowing practitioners to easily assess whether and which fair learning procedures result in classication outcomes that make groups better-o. Our analyses show that applying stricter fairness criteria codied as parity constraints can worsen welfare outcomes for both groups. More generally, always preferring "more fair" classiers does not abide by the Pareto Principle-a fundamental axiom of social choice theory and welfare economics. Recent work in machine learning has rallied around these notions of fairness as critical to ensuring that algorithmic systems do not have disparate negative impact on disadvantaged social groups. By showing that these constraints often fail to translate into improved outcomes for these groups, we cast doubt on their eectiveness as a means to ensure fairness and justice.
In a similar spirit, REF showed that always preferring "more fair" classifiers does not abide by the Pareto Principle (the principle that a policy must be preferable for at least one of multiple groups) in terms of welfare.
Fair classification and social welfare
Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency
Mathematics Computer Science Economics