@inproceedings{de9682d8e6644672924cdae041690003,
title = "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.",
keywords = "Algorithmic Fairness, Counterfactuals, Ethical AI, Ethics of AI, Explainable AI, Explanation, Fairness, Machine learning, Philosophy, Philosophy of AI, Social category, Social kind, Social ontology",
author = "Atoosa Kasirzadeh and Andrew Smart",
note = "Publisher Copyright: {\textcopyright} 2021 ACM.; 4th ACM Conference on Fairness, Accountability, and Transparency, FAccT 2021 ; Conference date: 03-03-2021 Through 10-03-2021",
year = "2021",
month = mar,
day = "3",
doi = "10.1145/3442188.3445886",
language = "English",
series = "FAccT 2021 - Proceedings of the 2021 ACM Conference on Fairness, Accountability, and Transparency",
publisher = "Association for Computing Machinery, Inc",
pages = "228--236",
booktitle = "FAccT 2021 - Proceedings of the 2021 ACM Conference on Fairness, Accountability, and Transparency",
}