  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.
