The Use and Misuse of Counterfactuals in Ethical Machine Learning
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.
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.
Research Areas
