Ira W. DeCamp Bioethics Seminars
ABSTRACT: Two criticisms of data science have recently emerged. The first argues that seemingly objective machine-learning algorithms often reinforce a racist or unjust status quo. The second chastises data science for failing to develop a science of causal inference rather than a mere collection of techniques for exploiting associations. The first highlights an urgent social problem; the second seems like an internal methodological dispute. I argue the absence of causal methodology in favor of a purely predictive approach in data science aids in the racialization of algorithms analogous to the history of racialized statistics. I propose a novel capabilities model of causal fairness to redress these concerns.