Debate about fairness in machine learning has largely centered around
competing definitions of what fairness or nondiscrimination between groups
requires. However, little attention has been paid to what precisely a group is.
Many recent approaches to "fairness" require one to specify a causal model of
the data generating process. These exercises make an implicit ontological
assumption that a racial or sex group is simply a collection of individuals who
share a given trait. We show this by exploring the formal assumption of
modularity in causal models, which holds that the dependencies captured by one
causal pathway are invariant to interventions on any other pathways. Causal
models of sex propose two substantive claims: 1) There exists a feature,
sex-on-its-own, that is an inherent trait of an individual that causally brings
about social phenomena external to it in the world; and 2) the relations
between sex and its effects can be modified in whichever ways and the former
feature would still retain the meaning that sex has in our world. We argue that
this ontological picture is false. Many of the "effects" that sex purportedly
"causes" are in fact constitutive features of sex as a social status. They give
the social meaning of sex features, meanings that are precisely what make sex
discrimination a distinctively morally problematic type of action. Correcting
this conceptual error has a number of implications for how models can be used
to detect discrimination. Formal diagrams of constitutive relations present an
entirely different path toward reasoning about discrimination. Whereas causal
diagrams guide the construction of sophisticated modular counterfactuals,
constitutive diagrams identify a different kind of counterfactual as central to
an inquiry on discrimination: one that asks how the social meaning of a group
would be changed if its non-modular features were altered.Comment: 11 pages, 5 figures, ACM Conference on Fairness, Accountability, and
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