The difficulties of causal reasoning and race
While the thorny ethical questions dogging the development and implementation of algorithmic decision systems touch on all manner of social phenomena, arguably the most widely discussed is that of racial discrimination. The watershed moment for the algorithmic ethics conversation was ProPublica's 2016 article on the COMPAS risk-scoring algorithm, and a huge number of ensuing papers in computer science, law, and related disciplines attempt to grapple with the question of algorithmic fairness by thinking through the role of race and discrimination in decision systems.
In a paper from earlier this year, ISSA KOHLER-HAUSMAN of Yale Law School examines the way that race and racial discrimination are conceived of in law and the social sciences. Challenging the premises of an array of research across disciplines, Kolher-Hausmann argues for both a reassessment of the basis of reasoning about discrimination, and a new approach grounded in a social constructivist view of race.
From the paper:
"This Article argues that animating the most common approaches to detecting discrimination in both law and social science is a model of discrimination that is, well, wrong. I term this model the 'counterfactual causal model' of race discrimination. Discrimination, on this account, is detected by measuring the 'treatment effect of race,' where treatment is conceptualized as manipulating the raced status of otherwise identical units (e.g., a person, a neighborhood, a school). Discrimination is present when an adverse outcome occurs in the world in which a unit is 'treated' by being raced—for example, black—and not in the world in which the otherwise identical unit is 'treated' by being, for example, raced white. The counterfactual model has the allure of precision and the security of seemingly obvious divisions or natural facts.
Currently, many courts, experts, and commentators approach detecting discrimination as an exercise measuring the counterfactual causal effect of race-qua-treatment, looking for complex methods to strip away confounding variables to get at a solid state of race and race alone. But what we are arguing about when we argue about whether or not statistical evidence provides proof of discrimination is precisely what we mean by the concept DISCRIMINATION."
- For an example of the logic Kohler-Hausmann is writing against, see Edmund S. Phelps' 1972 paper "The Statistical Theory of Racism and Sexism." Link.
- A recent paper deals with the issue of causal reasoning in an epidemiological study: "If causation must be defined by intervention, and interventions on race and the whole of SeS are vague or impractical, how is one to frame discussions of causation as they relate to this and other vital issues?" Link.
- From Kohler-Hausmann's footnotes, two excellent works informing her approach: first, the canonical book Racecraft by Karen Fields and Barbara Fields; second, a 2000 article by Tukufu Zuberi, "Decracializing Social Statistics: Problems in the Quantification of Race." Link to the first, link to the second.