The Racial Algorithm: AI and Scientific Legitimacy Under the Racial Contract

Recent debates in political philosophy of science underscore the need for normative frameworks to determine which values should guide scientific inquiry.1 Philosophers of science increasingly draw on principles from political philosophy, such as democratic majoritarianism, deliberative proceduralism, and Rawlsian public reason, to inform scientific investigations.2 The article argues these approaches fail in societies where basic institutions developed through historical racial domination, including chattel slavery, Jim Crow segregation, COINTELPRO’s targeting of Black movements, racially exclusionary housing policies, and mass incarceration without reparative correction. Drawing on Charles Mills’s (2017) critique of ideal theory, the article contends that these frameworks abstract away from the racialized causal structures that shape the data, models, and decisions of contemporary scientific and technological practices, and that the normative constraints they propose are insufficient to secure democratic legitimacy under conditions of unrepaired racial domination.3 Using Artificial Intelligence (AI) as a case study, the article introduces the concept of the Racial Algorithm to show how machine-learning systems fitted to racially structured training distributions can reproduce and compound racial inequities through algorithmic training and deployment. The article concludes by sketching a framework for scientific legitimacy grounded in nonideal theory.