What's Distributive Justice Got to Do with It? Rethinking Algorithmic Fairness from the Perspective of Approximate Justice

TL;DR

This paper critiques the simplistic link between algorithmic fairness and distributive justice, proposing a nuanced view that considers how deviations from ideal distributions are unfair, thus calling for a rethinking of fairness criteria.

Contribution

It introduces the concept of approximate justice in algorithmic fairness, emphasizing the importance of distribution of deviations from ideal fairness rather than just the ideal distribution itself.

Findings

Current fairness criteria are overly simplistic in linking to distributive justice.

Deviations from ideal fairness distributions are central to understanding algorithmic unfairness.

A new perspective on fairness focuses on the distribution of these deviations.

Abstract

In the field of algorithmic fairness, many fairness criteria have been proposed. Oftentimes, their proposal is only accompanied by a loose link to ideas from moral philosophy -- which makes it difficult to understand when the proposed criteria should be used to evaluate the fairness of a decision-making system. More recently, researchers have thus retroactively tried to tie existing fairness criteria to philosophical concepts. Group fairness criteria have typically been linked to egalitarianism, a theory of distributive justice. This makes it tempting to believe that fairness criteria mathematically represent ideals of distributive justice and this is indeed how they are typically portrayed. In this paper, we will discuss why the current approach of linking algorithmic fairness and distributive justice is too simplistic and, hence, insufficient. We argue that in the context of imperfect decision-making systems -- which is what we deal with in algorithmic fairness -- we should not only care about what the ideal distribution of benefits/harms among individuals would look like but also about how deviations from said ideal are distributed. Our claim is that algorithmic fairness is concerned with unfairness in these deviations. This requires us to rethink the way in which we, as algorithmic fairness researchers, view distributive justice and use fairness criteria.