The Use and Misuse of Counterfactuals in Ethical Machine Learning

TL;DR

This paper critically examines the use of counterfactuals in ethical machine learning, highlighting potential issues when social categories like race or gender are involved, and offers guidelines for their appropriate use.

Contribution

It provides a philosophical and social science review showing the limitations of counterfactuals in social categories, and proposes tenets for their cautious application in fairness and explainability.

Findings

Counterfactuals may not coherently represent social categories.

Misuse of counterfactuals can lead to misleading fairness assessments.

Guidelines are proposed for responsible use of counterfactuals in social contexts.

Abstract

The use of counterfactuals for considerations of algorithmic fairness and explainability is gaining prominence within the machine learning community and industry. This paper argues for more caution with the use of counterfactuals when the facts to be considered are social categories such as race or gender. We review a broad body of papers from philosophy and social sciences on social ontology and the semantics of counterfactuals, and we conclude that the counterfactual approach in machine learning fairness and social explainability can require an incoherent theory of what social categories are. Our findings suggest that most often the social categories may not admit counterfactual manipulation, and hence may not appropriately satisfy the demands for evaluating the truth or falsity of counterfactuals. This is important because the widespread use of counterfactuals in machine learning can lead to misleading results when applied in high-stakes domains. Accordingly, we argue that even though counterfactuals play an essential part in some causal inferences, their use for questions of algorithmic fairness and social explanations can create more problems than they resolve. Our positive result is a set of tenets about using counterfactuals for fairness and explanations in machine learning.