Blog · Review Essay · Last reviewed June 14, 2026

Resisting AI and the Politics of Refusal

Dan McQuillan's Resisting AI is not another plea for fairer dashboards or friendlier model documentation. It asks a harder question: what if some automated systems should be refused because they extend the institutional habits that already make people sortable, suspicious, cheap, and governable?

The Book

Resisting AI: An Anti-fascist Approach to Artificial Intelligence was published by Bristol University Press/Policy Press in 2022. The publisher lists the paperback at 190 pages with ISBN 978-1529213508, while Oxford Academic lists the Policy Press Scholarship Online record with DOI 10.1332/policypress/9781529213492.001.0001, online ISBN 9781529213539, and a July 15, 2022 publication date.

McQuillan is a lecturer in creative and social computing at Goldsmiths, University of London. The publisher biography is relevant because the book does not come from a narrow technical-audit tradition: it draws on physics, computing, care work, mental-health advocacy, asylum information projects, citizen science, Amnesty International, data justice, and political theory.

The book appeared just before the ChatGPT boom made generative AI a daily interface. That timing makes it more useful, not less. Its core target is not one chatbot product or one model family. It is the social logic of deep learning when it is adopted by institutions that already prefer measurable proxies, austerity administration, security theater, predictive suspicion, and optimization without accountability.

The Machine as Institution

Resisting AI is strongest when it treats AI as a political arrangement rather than a neutral capability. McQuillan does not begin from the usual question, "How accurate is the model?" He begins from the setting that gives the model power: the welfare office, border system, workplace, school, police program, health triage workflow, content platform, or procurement process that wants a technical object to make hard social decisions appear manageable.

This aligns the book with Automating Inequality, Race After Technology, Seeing Like a State, and The Tyranny of Metrics. In each case, the danger is not only that a tool makes an error. The deeper danger is that an institution builds a world in which the tool's categories become the terms on which people must survive.

A model can turn old records into a prediction. An agency can turn the prediction into a queue. A manager can turn the queue into a performance target. A person can then be forced to behave inside a category made from someone else's data. The loop is political before it is technical. It decides whose reality is considered evidence, whose account is treated as noise, and who gets a usable path to appeal.

Optimization and Bureaucracy

One of the book's useful moves is to connect AI to bureaucracy rather than treating it as a break from bureaucracy. The LSE review by Milan Stürmer and Mark Carrigan emphasizes this point: McQuillan reads AI less as a science-fiction rupture than as an upgrade to existing administrative order. That is the right lens for many real deployments.

Optimization sounds technical, but in institutions it often means choosing which value will be maximized and which costs will be made external. A benefits system may optimize for fraud reduction and push risk onto families. A workplace platform may optimize for throughput and push instability onto workers. A school may optimize for detection and push suspicion onto students. A border system may optimize for risk and turn a life story into a machine-readable case file.

This is why "AI ethics" can become too narrow. Bias audits, transparency statements, and fairness metrics can help in some cases, but they cannot by themselves answer whether a system should exist, whether the institution using it deserves the power it gains, or whether the people affected can actually contest the output. A more legible cage is still a cage.

The Labor and Matter of AI

McQuillan's chapter abstracts make clear that his account of AI includes data dependence, neural-network opacity, carbon emissions, centralization of control, underpaid human labor, and the anti-worker politics that can accompany automation. That places the book beside Atlas of AI, The Eye of the Master, and Ghost Work.

The point is not that AI is fake because workers are involved. The point is that the public story of automation routinely erases the workers, data subjects, maintainers, moderators, labelers, miners, and energy systems that make automation possible. A system marketed as intelligence can function as a redistribution machine: attention, risk, exhaustion, and environmental cost move downward and outward while authority moves upward into platforms and agencies.

That matters for human-machine cognition. A model is not just a mathematical artifact sitting apart from society. It is a way of organizing perception and action across many people and machines. When the organizational design is extractive, the resulting intelligence inherits that design. It can learn from care while making care workers disposable. It can learn from public culture while enclosing the benefits. It can learn from collective activity while giving the collective no governing role.

The Force of the Title

The title is easy to caricature. The useful reading is not that every statistical model is fascist. It is that AI, as currently organized, can mesh dangerously with authoritarian, eugenic, and austerity-driven habits: ranking lives, normalizing exception, sorting populations, treating care as cost, and presenting exclusion as objective necessity.

This is where the book's polemical force helps. Many AI debates stay inside reform language: make the dataset better, add a human in the loop, improve explainability, publish a principle, audit the vendor. McQuillan presses on the prior question. If an institution is using AI to intensify scarcity, automate suspicion, or evade responsibility, then the problem is not a missing fairness patch. The problem is the project.

That does not mean the strongest argument is always the most maximal one. The word "fascist" should remain tied to concrete mechanisms: emergency powers, hierarchy, racialized and ableist valuation, forced legibility, policing, border control, labor discipline, and the shrinking of democratic space. Used carefully, the term names a pattern. Used lazily, it can flatten important differences between systems and weaken the case for refusal.

Refusal as Governance

The book's most valuable contribution is its insistence that refusal is not the absence of governance. Refusal can be governance. A community can say that a predictive policing system should not be purchased. Workers can reject an algorithmic management system that turns the job into surveillance. Patients can resist a triage system that hides rationing behind a score. Public agencies can decide that some forms of automated eligibility, risk assessment, or biometric sorting are incompatible with democratic service.

McQuillan's alternatives include workers' councils, people's councils, mutual aid, commons, feminist care, decolonial knowledge, and forms of "decomputing" that constrain the reach of automation. These proposals will frustrate readers looking for a procurement checklist. They are not vendor-neutral best practices. They are a demand that affected people gain power over whether a system is built, bought, deployed, repaired, or shut down.

That demand belongs in AI governance because otherwise governance becomes a paperwork layer around inevitability. If the only choices are "deploy now" or "deploy after compliance review," the public has already lost the most important decision. A real governance regime must preserve the word no.

Where the Book Needs Friction

The book's strength is also its weakness. It is an urgent political argument, and urgency can compress technical variation. Predictive scoring, recommender systems, facial recognition, fraud detection, generative models, robotic systems, and decision-support tools do not all work the same way or create the same harms. Serious resistance needs specificity.

The LSE reviewers make a related criticism: they credit the book for raising serious concerns about bias and misuse, but argue that it lacks enough detailed technical analysis to support some of its social claims. That critique should be taken seriously. The more severe the charge, the more important it is to show the mechanism, the deployment path, the affected people, and the institutional incentives.

Still, the answer is not to retreat into technical modesty. The better reading is to pair McQuillan's political theory of refusal with the evidentiary discipline of books like AI Snake Oil, the administrative realism of Recoding America, and the power analysis of The Tech Coup. Refusal is strongest when it can name exactly what is being refused and why.

The AI-Age Reading

Read after the generative-AI boom, Resisting AI becomes a warning about agentic bureaucracy. Chatbots and copilots enter organizations as helpful surfaces: summarizers, triage assistants, writing tools, coding agents, tutors, casework aids, customer-service bots, and policy interpreters. The interface may feel conversational, but the system often inherits the organization's incentives.

If a benefits office is underfunded, an AI assistant may make scarcity look smoother. If a company is trying to deskill support labor, a chatbot may make degraded service look innovative. If a school does not trust students, an AI detector may make suspicion feel scientific. If a platform wants cheaper moderation, a model may hide the remaining human cost. The danger is not that machines become evil. The danger is that institutions become more effective at bad habits.

The practical lesson is simple: ask what power the system extends. Does it give affected people more voice, appeal, context, and collective leverage? Or does it make them more measurable, more replaceable, more governable, and easier to ignore? Does the model open a decision to democratic contest, or does it turn contest into an exception-handling ticket?

McQuillan's book is not the final word on AI. It is a necessary irritant. It refuses the assumption that every social problem should be translated into prediction, every public function into optimization, every worker into a metric source, and every democratic disagreement into a technical implementation issue. In an AI culture addicted to capability talk, that refusal is a form of realism.

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