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Invited Talk: Does Causal Thinking About Discrimination Assume a Can Opener?
Lily Hu

Increasingly, proof of whether systems, algorithmic and not, are racially discriminatory typically takes the form of statistical evidence supposedly showing race to have causally influenced some outcome. In this talk, I will discuss the relationship between quantitative social scientific methods on causal effects of race and our normative thinking about racial discrimination. I argue that all causal inference methodologies that look to quantify causal effects of race embed what amount to substantive views about what race as a social category is and how race produces effects in the world. Though debates among causal inference methodologists are often framed as concerning which practices make for good statistical hygiene, I suggest that quantitative methods are much more straightforwardly normative than most scholars, social scientists and philosophers alike, have previously appreciated.

Thinking causally about race is, I want to suggest, at least just as hard as the substantive discrimination question. For answering the question about race and causation in the social world, requires answers to substantive normative questions about race, racial discrimination, and racial injustice more broadly. And so thinking about how race acts causally is not easier or even a helpful reduction for answering the moral and political question. If we’ve “solved” the causal problem, we’ve “solved” the substantive normative questions about race, racial discrimination, and racial injustice more broadly. It reminds one of the following joke:

A physicist, a chemist, and an economist who are stranded on a desert island with no implements and a can of food. The physicist and the chemist each devise an ingenious mechanism for getting the can open. The economist says, "Assume we have a can opener!"

My argument is that tackling the racial discrimination problem by assuming we can draw a diagram of how race acts causally in the world is a bit like that: it is to assume we have what it is that we precisely need; it is to assume a can opener!

Author Information

Lily Hu (Harvard University)

Lily Hu is a PhD candidate in Applied Mathematics and Philosophy at Harvard University. She works on topics in machine learning, algorithmic fairness, and (political) philosophy of technology. Her current time is divided between computer science-related research, where she studies theoretical properties and behaviors of machine learning systems as they bear on deployment in social and economic settings, and philosophical work, where she thinks about causal reasoning about categories like race, theories of discrimination, and what about current technological trends makes capitalism even more distressing.

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