Discussions of the causal status of race focus on the question of whether race itself can be experimentally manipulated. Yet many experiments testing for racial discrimination do not manipulate race, but rather a signal by which race influences an outcome. Such signal manipulations are easily formalized, though contexts of discrimination introduce significant philosophical complications. Whether a signal counts as a signal for race is not merely a causal question, but depends on sociological and normative issues regarding discrimination. The notion of (...) signal manipulation enables one to take these issues into account while still using causal counterfactual tests to detect discrimination. The analysis provided here is compatible with social constructivism and helps differentiate between cases in which it is more or less fruitful to model race causally. (shrink)

In this Special Issue of Journal of Critical Realism on Normativity, Elder-Vass has provided a paper that in part responds to one that Chris Winship and I wrote together, which was presented at the...

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 wide- spread 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. (shrink)