{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,14]],"date-time":"2026-08-14T15:56:27Z","timestamp":1786722987270,"version":"build-2736575974"},"reference-count":45,"publisher":"The Pennsylvania State University Press","issue":"1","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2026,6,19]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>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\u2019s targeting of Black movements, racially exclusionary housing policies, and mass incarceration without reparative correction. Drawing on Charles Mills\u2019s (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.<\/jats:p>","DOI":"10.5325\/critphilrace.14.1.0126","type":"journal-article","created":{"date-parts":[[2026,7,2]],"date-time":"2026-07-02T17:58:48Z","timestamp":1783015128000},"page":"126-153","source":"Crossref","is-referenced-by-count":1,"title":["The Racial Algorithm: AI and Scientific Legitimacy Under the Racial Contract"],"prefix":"10.5325","volume":"14","author":[{"given":"Alexander Williams","family":"Tolbert","sequence":"first","affiliation":[{"name":"Emory University, United States of America"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"3287","reference":[{"key":"2026070213514822800_r1","article-title":"\u201cDismantling the \u2018Black Opticon\u2019: Privacy, Race Equity, and Online Data-Protection Reform.\u201d","year":"2022"},{"key":"2026070213514822800_r2","unstructured":"Angwin, Julia, JeffLarson, SuryaMattu, and LaurenKirchner. 2016. \u201cMachine Bias.\u201dProPublica, May23, 2016. www.propublica.org\/article\/machine-bias-risk-assessments-in-criminal-sentencing."},{"issue":"3","key":"2026070213514822800_r3","first-page":"377","article-title":"\u201cScience and Values in the Regulatory Process.\u201d","volume":"3","year":"1988","journal-title":"Statistical Science"},{"key":"2026070213514822800_r4","doi-asserted-by":"crossref","first-page":"1133","DOI":"10.1007\/s11098-025-02323-x","article-title":"\u201cPublic Reason, Values in Science, and the Shifting Boundaries of the Political Forum.\u201d","volume":"182","year":"2025","journal-title":"Philosophical Studies"},{"issue":"3","key":"2026070213514822800_r5","first-page":"671","article-title":"\u201cBig Data\u2019s Disparate Impact.\u201d","volume":"104","year":"2016","journal-title":"California Law Review"},{"issue":"4","key":"2026070213514822800_r6","doi-asserted-by":"crossref","first-page":"991","DOI":"10.1257\/0002828042002561","article-title":"\u201cAre Emily and Greg More Employable than Lakisha and Jamal? A Field Experiment on Labor Market Discrimination.\u201d","volume":"94","year":"2004","journal-title":"American Economic Review"},{"key":"2026070213514822800_r7","article-title":"\u201cRecovering from Biased Data: Can Fairness Constraints Improve Accuracy?\u201d","year":"2020"},{"issue":"5","key":"2026070213514822800_r8","doi-asserted-by":"crossref","first-page":"977","DOI":"10.1177\/0003122417725865","volume":"82","year":"2017","journal-title":"American Sociological Review"},{"key":"2026070213514822800_r9","doi-asserted-by":"crossref","first-page":"2227","DOI":"10.1007\/s11229-017-1333-z","article-title":"\u201cDu Bois\u2019 Democratic Defence of the Value Free Ideal.\u201d","volume":"195","year":"2018","journal-title":"Synthese"},{"issue":"1","key":"2026070213514822800_r10","doi-asserted-by":"crossref","first-page":"4209","DOI":"10.1038\/s41598-022-07939-1","article-title":"\u201cA Clarification of the Nuances in the Fairness Metrics Landscape.\u201d","volume":"12","year":"2022","journal-title":"Scientific Reports"},{"key":"2026070213514822800_r11","doi-asserted-by":"crossref","first-page":"3743","DOI":"10.1007\/s43681-025-00680-1","article-title":"\u201cHunting for Humans: On Slavery, the Emergence of the US as the World\u2019s First Super Industrial State and Its Deployment of Artificial Intelligence and Other Military Technology to Repress Dissent and Neutralize Enemy Combatants.\u201d","volume":"5","year":"2025","journal-title":"AI and Ethics"},{"key":"2026070213514822800_r12","unstructured":"Coate, Stephen, and Glenn C.Loury. 1993. \u201cWill Affirmative-Action Policies Eliminate Negative Stereotypes?\u201dAmerican Economic Review83 (5): 1220\u201340. https:\/\/www.jstor.org\/stable\/2117558."},{"key":"2026070213514822800_r13","unstructured":"Curry, Tommy J.\n          2011. \u201cThe Political Economy of Reparations: An Anti-Ethical Consideration of Atonement and Racial Reconciliation under Colonial Moralism.\u201dRace, Gender & Class18 (1\/2): 125\u201346. http:\/\/www.jstor.org\/stable\/23884871."},{"issue":"4","key":"2026070213514822800_r14","doi-asserted-by":"crossref","first-page":"559","DOI":"10.1086\/392855","article-title":"\u201cInductive Risk and Values in Science.\u201d","volume":"67","year":"2000","journal-title":"Philosophy of Science"},{"key":"2026070213514822800_r15","unstructured":"Douglas, Heather E.\n          2008. \u201cThe Role of Values in Expert Reasoning.\u201dPublic Affairs Quarterly22 (1): 1\u201318. https:\/\/www.jstor.org\/stable\/40441475."},{"key":"2026070213514822800_r16","volume-title":"Science, Policy, and the Value-Free Ideal","year":"2009"},{"key":"2026070213514822800_r17","first-page":"83","year":"2017","journal-title":"Eppur si Muove: Doing History and Philosophy of Science with Peter Machamer"},{"key":"2026070213514822800_r18","first-page":"ix","volume-title":"Exploring Inductive Risk: Case Studies of Values in Science","year":"2017"},{"issue":"1","key":"2026070213514822800_r19","doi-asserted-by":"crossref","first-page":"eaao5580","DOI":"10.1126\/sciadv.aao5580","article-title":"\u201cThe Accuracy, Fairness, and Limits of Predicting Recidivism.\u201d","volume":"4","year":"2018","journal-title":"Science Advances"},{"issue":"7","key":"2026070213514822800_r22","doi-asserted-by":"crossref","first-page":"647","DOI":"10.1289\/ehp.1408107","article-title":"\u201cScience, Policy, and the Transparency of Values.\u201d","volume":"122","year":"2014","journal-title":"Environmental Health Perspectives"},{"key":"2026070213514822800_r23","volume-title":"Automating Inequality: How High-Tech Tools Profile, Police, and Punish the Poor","year":"2018"},{"issue":"2","key":"2026070213514822800_r24","doi-asserted-by":"crossref","first-page":"366","DOI":"10.1111\/joca.12297","article-title":"\u201cDigital Redlining and the Fintech Marketplace: Evidence from US Zip Codes.\u201d","volume":"55","year":"2021","journal-title":"Journal of Consumer Affairs"},{"key":"2026070213514822800_r25","unstructured":"Fryer, Daniel. 2020. \u201cRace, Reform, and Progressive Prosecution.\u201dThe Journal of Criminal Law and Criminology110 (4): 769\u2013802. https:\/\/www.jstor.org\/stable\/48595414."},{"key":"2026070213514822800_r26","doi-asserted-by":"crossref","first-page":"553","DOI":"10.1007\/s11572-023-09700-9","article-title":"\u201cIdealizing Abolition.\u201d","volume":"17","year":"2023","journal-title":"Criminal Law and Philosophy"},{"issue":"1","key":"2026070213514822800_r27","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1111\/jofi.13090","article-title":"\u201cPredictably Unequal? The Effects of Machine Learning on Credit Markets.\u201d","volume":"77","year":"2022","journal-title":"Journal of Finance"},{"key":"2026070213514822800_r28","article-title":"\u201cPushing the Limits of Fairness Impossibility: Who\u2019s the Fairest of Them All?\u201d","year":"2022","journal-title":"Proceedings of the 36th Conference on Neural Information Processing Systems (NeurIPS 2022)"},{"issue":"4","key":"2026070213514822800_r29","doi-asserted-by":"crossref","first-page":"1145","DOI":"10.1093\/bjps\/axy007","article-title":"\u201cWhat Is Epistemic Public Trust in Science?\u201d","volume":"70","year":"2019","journal-title":"The British Journal for the Philosophy of Science"},{"key":"2026070213514822800_r30","unstructured":"Kleinberg, Jon, SendhilMullainathan, and ManishRaghavan. 2016. \u201cInherent Trade-Offs in the Fair Determination of Risk Scores.\u201dhttps:\/\/arxiv.org\/abs\/1609.05807."},{"key":"2026070213514822800_r31","doi-asserted-by":"crossref","first-page":"14","DOI":"10.1111\/j.1740-9713.2016.00960.x","article-title":"\u201cTo Predict and Serve?\u201d","volume":"13","year":"2016","journal-title":"Significance"},{"key":"2026070213514822800_r32","doi-asserted-by":"crossref","first-page":"102","DOI":"10.1016\/j.shpsa.2021.08.009","article-title":"\u201cDoes Democracy Require Value-Neutral Science? 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