Machine learning algorithms bring out an under-appreciated puzzle of discrimination, namely, figuring when a decision made on the basis of a factor correlated with race is a decision made on the basis of race. I argue that prevailing approaches, which are based in identifying and then distinguishing among causal effects of race, in their metaphysical timidity, fail to get off the ground. I suggest, instead, that adopting a constructivist theory of race answers this puzzle in a principled manner. On what I call a “thick constructivist” account of race, to be raced is to be socially positioned in the way indicated by a certain set of statistical regularities on the basis of particular phenotypic traits. A thick constructivist sees that acting on the basis of correlations that constitute race qua social position just is acting on the basis of race, because races just are social positions that subject their member individuals to a particular matrix of social relations that define the raced position. This conclusion has considerable ramifications for our understanding of discrimination, algorithmic and beyond.
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