Shortcomings of Counterfactual Fairness and a Proposed Modification

TL;DR

This paper critiques the limitations of counterfactual fairness, demonstrating it is not necessary for fairness, and proposes a new causal relevance fairness constraint to address these issues.

Contribution

It introduces causal relevance fairness as a novel modification to counterfactual fairness to better capture intuitive notions of algorithmic fairness.

Findings

Counterfactual fairness is not a necessary condition for fairness.

A hypothetical scenario illustrates the disconnect between counterfactual fairness and intuitive fairness.

Causal relevance fairness aims to improve fairness constraints by addressing counterfactual fairness's shortcomings.

Abstract

In this paper, I argue that counterfactual fairness does not constitute a necessary condition for an algorithm to be fair, and subsequently suggest how the constraint can be modified in order to remedy this shortcoming. To this end, I discuss a hypothetical scenario in which counterfactual fairness and an intuitive judgment of fairness come apart. Then, I turn to the question how the concept of discrimination can be explicated in order to examine the shortcomings of counterfactual fairness as a necessary condition of algorithmic fairness in more detail. I then incorporate the insights of this analysis into a novel fairness constraint, causal relevance fairness, which is a modification of the counterfactual fairness constraint that seems to circumvent its shortcomings.