Science, Values, and Race Correction

Race correction, or the use of race as a predictor in clinical algorithms, risk predictor tools, and practice guidelines to estimate health risks, is highly contentious in the US Critics of race correction defend a position I call “eliminativism”: They argue that we should immediately remove race from clinical algorithms. Proponents of race correction defend a position I call “conservationism”: They insist that we should retain the use of race until better predictors of health outcomes become available. In this chapter, I argue that the classic argument from inductive risk is a useful tool for disambiguating at least part of these competing claims in the race correction debate and understanding the role of values in guiding methodological choices in the construction of clinical algorithms. Readers may be interested in these Handbook chapters as well: Yasmin Haddad and Celso Neto, “Values in Human Genomics”; Alexander Tolbert, “Algorithmic Abolitionism and The Racial Algorithm.”