Artificial Whiteness and the Ideology Called AI
Yarden Katz's Artificial Whiteness is not a standard book about biased algorithms. It is a critique of artificial intelligence as a flexible institutional idea: a label that gathers funding, expertise, military ambition, corporate futurism, carceral reform, and racialized models of knowledge into a package that can be sold as technical necessity.
The Book
Artificial Whiteness: Politics and Ideology in Artificial Intelligence was published by Columbia University Press in November 2020. Columbia lists the paperback ISBN as 9780231194914, the hardcover ISBN as 9780231194907, the e-book ISBN as 9780231551076, and the book at 352 pages. JSTOR's book record shows the internal structure: formation, self and social order, and alternatives, with chapters on empire, capital, epistemic forgeries, carceral-positive reform, artificial whiteness, dissenting visions, and refusal.
Katz's current University of Michigan profile places the work in American Culture and Digital Studies, with fields including imperialism, white supremacy, racial capitalism, science and technology studies, biomedicine, eugenics, exploited labor in science, and radical social movements. That location matters. This is a book about AI history, but it is also a book about the institutions that make some definitions of intelligence useful, fundable, and politically convenient.
The review belongs beside Atlas of AI, Race After Technology, Algorithms of Oppression, Dark Matters, Surveillance Valley, and The Cultural Logic of Computation. Those books ask how computation becomes power. Katz presses harder on the premise that artificial intelligence is a coherent technical destiny at all.
The AI Label
The strongest move in Artificial Whiteness is to treat "AI" as a historical label with political work to do. Katz is not saying that software, machine learning, statistics, robotics, and neural networks are imaginary. The point is sharper: the phrase artificial intelligence has repeatedly expanded, narrowed, disappeared, and returned according to the needs of funders, universities, firms, the military, and professional experts.
That makes the question less mystical. Instead of asking only whether a system is truly intelligent, ask what becomes easier once it is called AI. A research program can attract defense money. A company can rebrand data extraction as futurism. A university center can become a policy authority. A policing system can present itself as objective analysis. A welfare tool can appear modern rather than punitive. A workplace dashboard can become an intelligent manager rather than a managerial choice.
This is why Katz is useful in the present moment. The AI label does not merely describe a capability. It can authorize a relationship. It lets institutions say the machine has arrived, society must adapt, experts must manage the transition, and old political arguments are now technical implementation problems.
That argument pairs naturally with AI Snake Oil, but it is aimed at a deeper layer. Narayanan and Kapoor ask what AI systems have actually proved. Katz asks why so many institutions want the label to carry authority before proof, and why the label survives even when its technical content keeps changing.
Whiteness as Method
The title's difficult term is doing analytical work. Katz is not using whiteness as a simple synonym for the skin color of individual engineers. The book treats whiteness as an ideological form: a way of making a historically situated, racialized, gendered, imperial, and capitalist viewpoint appear universal, neutral, placeless, and entitled to rule.
This maps onto AI in concrete ways. A benchmark can be treated as a universal test of intelligence even when it encodes narrow institutional priorities. A game-playing system can be treated as evidence about thought in general. A facial-recognition improvement can be framed as inclusion while extending the reach of biometric control. A prediction system can claim neutrality while turning old records into future suspicion. A model can appear to know autonomously while hiding the people, data, objectives, funding, and deployment setting that made its output possible.
Katz's phrase "epistemic forgeries" is useful here. The forgery is not just a false statement. It is a counterfeit form of knowledge that lets power act while appearing detached from power. AI becomes a view from nowhere, a measure of universal cognition, and an autonomous decision-maker. Each move weakens accountability because no one has to say plainly: this institution chose this model, trained it on these records, pointed it at these people, and treated the result as authority.
That is the book's best contribution to AI criticism. Bias is not only a defect inside a model. Bias can be a function of the institutional project that asked the model to exist.
Carceral-Positive Logic
The chapter on carceral-positive logic is the hinge of the book. Katz argues that some critical AI work can end up improving the legitimacy of harmful systems by making them more technically refined. A facial-recognition system that performs more evenly across demographic groups may still expand surveillance. A risk score with better calibration may still make cages, raids, watchlists, and deprivation look like neutral administrative outputs. A fairness audit may turn an abolition question into a vendor remediation ticket.
This is not an argument against measuring harms. It is an argument against letting measurement define the moral horizon. If the institution is doing violent work, making the classifier more accurate may make the violence more durable. If the problem is that police, prisons, borders, landlords, employers, insurers, or schools have too much unaccountable power, then the ethical question cannot stop at whether the software is less biased than its previous version.
The Information & Culture review highlights Katz's attention to the Stop LAPD Spying Coalition, whose organizing against Los Angeles predictive-policing systems becomes an example of community research refusing the premises of data-driven policing. WIRED's reporting on Operation LASER and PredPol gives the operational context: historical police data, scoring, hotspots, and targeted attention can route more policing toward communities already exposed to police contact.
That is the recursive danger. The system records contact, interprets contact as risk, sends more attention, creates more records, and then treats the record trail as evidence. Calling the loop AI makes it feel as if intelligence has discovered danger. In practice, an institution may have automated its own suspicion.
Recursive Reality
Artificial Whiteness is especially useful for thinking about recursive reality because it shows how a category becomes infrastructure. "AI" starts as a research label, becomes a funding magnet, becomes an expert identity, becomes a policy object, becomes a procurement category, becomes a public fear, becomes an ethics industry, and then becomes evidence that society must organize around AI.
The loop is not only discursive. It changes budgets, careers, conferences, standards, grant programs, vendor roadmaps, police tools, university centers, classroom assignments, military planning, and public vocabulary. Once an institution has an AI office, AI strategy, AI committee, AI procurement line, and AI ethics policy, the premise has already won a great deal. The world has been rearranged so that AI appears to be the thing everyone must respond to.
That pattern should be familiar from other machine-readable systems. A ranking creates ranking behavior. A dashboard creates dashboard work. A benchmark creates benchmark training. A risk model creates risk records. An answer engine creates source behavior around answer extraction. A label creates the institution that then proves the label was real.
Katz adds a harder political question: who benefits when the label becomes real, and who loses the ability to name the underlying institution?
The AI Reading
Read in 2026, Artificial Whiteness is a warning about the governance language around foundation models, agents, and automated decision systems. The phrase "AI governance" can mean democratic control over technical systems. It can also become a way for technical experts, vendors, consultants, universities, and state agencies to professionalize the management of systems whose deeper institutional purposes remain untouched.
The problem is visible whenever a product turns a political choice into an AI-readiness question. Should a school surveil students? Should a court score defendants? Should a welfare office automate suspicion? Should a workplace instrument every keystroke? Should a city fuse cameras, license-plate readers, emergency calls, and predictive maps? Should a border agency make asylum seekers machine-readable before their stories are heard?
The weak version of AI ethics asks whether the model performs fairly enough. Katz pushes toward the prior question: why is this institution seeking this form of machine authority at all?
That question does not make technical work irrelevant. It makes technical work answerable. Evaluation, red teaming, documentation, audits, model cards, data sheets, and impact assessments matter only if they preserve the option to stop, shrink, redesign, or refuse the system. Without that option, governance becomes a ceremony that makes deployment look responsible.
Where the Book Needs Friction
Artificial Whiteness is polemical, and the polemic is both its force and its risk. It is strongest when it shows how AI talk can hide institutional violence behind technical inevitability. It is weaker if read as a complete taxonomy of every technical system that has ever traveled under the AI label.
Technical differences still matter. Expert systems, statistical machine learning, search ranking, recommender systems, facial recognition, language models, robotics, optimization tools, and agent frameworks do not all work the same way. Their failure modes, dependencies, labor politics, energy costs, legal duties, and governance levers differ. A critique of the AI label should not flatten those differences after showing how the label itself flattens politics.
The book also asks a lot of readers. Its argument depends on critical race theory, histories of empire, histories of AI, science studies, abolitionist critique, and political economy. Joshua K. Smith's Prometheus review is useful here because it praises the book's value while pressing on the practical difficulty of Katz's refusal politics. That is a real tension. Refusal can be clarifying, but institutions also need concrete ways to redirect money, shut down harmful systems, preserve useful tools, protect workers, and give affected communities operational power.
Those limits do not make the book less important. They make its best use clearer. Read Katz as a diagnostic for the authority that gathers around AI, not as a substitute for technical analysis, organizing strategy, procurement rules, labor policy, or democratic institution-building.
What This Changes
The practical lesson is to audit the institutional project before auditing only the model.
When a system is presented as AI, ask what the word is doing. Does it attract money? Deflect scrutiny? Convert a political problem into a technical problem? Create a new expert class? Make an old institution look modern? Turn coercion into service delivery? Make refusal seem irresponsible because the future has supposedly arrived?
Then ask what would count as success for the people most exposed to the system. Better accuracy may not be success. More representative training data may not be success. A cleaner dashboard may not be success. Success may mean fewer surveillance points, fewer automated denials, fewer carceral pathways, stronger appeal rights, smaller datasets, public ownership, worker control, community veto power, or no system at all.
Artificial Whiteness matters because it refuses to let AI stand as a natural event. The machine does not simply arrive. It is named, funded, narrated, deployed, repaired, defended, and normalized by institutions. Once that is visible, the question changes from how to adapt to AI to who is using the idea of AI, against whom, and for what world.
Sources
- Columbia University Press, Artificial Whiteness: Politics and Ideology in Artificial Intelligence, publisher record, publication date, formats, ISBNs, page count, description, reviews, and contents, reviewed June 14, 2026.
- JSTOR, Artificial Whiteness: Politics and Ideology in Artificial Intelligence, book record and chapter listing, reviewed June 14, 2026.
- University of Michigan LSA American Culture, Yarden Azoulay Katz profile, fields of study, current institutional context, and book list, reviewed June 14, 2026.
- Jasmine Clark, review of Artificial Whiteness, College & Research Libraries, volume 82, issue 5, 2021, DOI 10.5860/crl.82.5.775, reviewed June 14, 2026.
- Gregory Laynor, review of Artificial Whiteness, Information & Culture, University of Texas Press, reviewed June 14, 2026.
- Joshua K. Smith, review of Artificial Whiteness, Prometheus 38, no. 2, pages 266-270, 2022, DOI 10.13169/prometheus.38.2.0266, reviewed June 14, 2026.
- Roberto Sirvent, "BAR Book Forum: Yarden Katz's Book Artificial Whiteness", Black Agenda Report, October 21, 2020, reviewed June 14, 2026.
- Issie Lapowsky, "How the LAPD Uses Data to Predict Crime", WIRED, May 22, 2018, reviewed June 14, 2026.
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