Artificial Unintelligence and the Politics of Technochauvinism
Meredith Broussard's Artificial Unintelligence is a practical antidote to machine enchantment. Its central lesson is not that computers are useless. It is that institutions become dangerous when they treat computation as proof of superior judgment.
The Book
Artificial Unintelligence: How Computers Misunderstand the World was published by MIT Press in 2018. MIT Press lists the hardcover publication date as April 27, 2018, the paperback as January 29, 2019, and the book at 248 pages. The Press describes it as a guide to the inner workings and outer limits of technology, written by a software developer and journalist who argues against assuming that computers reliably get things right.
Broussard is an associate professor at NYU's Arthur L. Carter Journalism Institute and research director at the NYU Alliance for Public Interest Technology. Her background matters because the book is not a rejection of technical knowledge from outside the field. It is a programmer's argument for better judgment about what computation can do, what it cannot do, and what social damage follows when people confuse automation with intelligence.
The book won the 2019 PROSE Award in Computing and Information Sciences, according to the PROSE Awards list and MIT Press's awards page. Its durability comes from a plain diagnosis: many failures sold as AI breakthroughs are actually failures of institutions, incentives, data, assumptions, maintenance, and accountability.
Technochauvinism
Broussard's key term is technochauvinism: the belief that technological solutions are inherently superior. That belief is less crude than it sounds. It often appears as a management habit, a funding preference, a procurement slogan, a product demo, or a reform plan that treats social complexity as a backlog item waiting for software.
This is why the book belongs beside To Save Everything, Click Here, The Technological Society, and Weapons of Math Destruction. Each asks what happens when a technical method receives authority before the surrounding institution has earned trust.
Broussard's tone is useful because it avoids mystical fear. Computers are powerful symbolic machines. They are also literal machines with brittle inputs, narrow representations, maintenance burdens, biased histories, security problems, and human organizations around them. The myth begins when people look at the first fact and stop inspecting the rest.
How Computers Misunderstand
The title's force is in the word "misunderstand." Computers do not misunderstand in the human sense. They process formal representations. The mistake is ours when we feed messy human situations into narrow computational forms and then treat the output as if it contained the missing context.
The book's examples move across driverless cars, machine learning, standardized tests, campaign finance data, and programming practice. MIT Press's summary notes that Broussard tests AI against standardized-test problems, uses machine learning on the Titanic survival dataset, and attempts to build software for campaign-finance investigation. Those examples make the same point from different angles: impressive computation can still fail when the problem is underspecified, the data is partial, or the world does not match the model's assumptions.
That lesson is easy to forget in the generative AI era because interfaces now talk back fluently. A chatbot can make misunderstanding look like comprehension. A system can summarize, advise, rank, or explain without having a grounded grasp of the social situation it is being asked to govern.
The Institutional Problem
Artificial Unintelligence is strongest when read as institutional criticism. The danger is not only that a model makes mistakes. People make mistakes too. The danger is that automated mistakes can be wrapped in procurement authority, scaled through workflows, hidden behind vendor secrecy, and normalized as progress.
A public agency can buy a system before it knows how to audit it. A school can adopt a platform because dashboards look more objective than teacher judgment. A newsroom can chase automation while weakening the reporting labor that would check the data. A company can frame deskilling as modernization. In each case, the computer is not acting alone. It is lending prestige to an institution that wants speed, certainty, savings, or legitimacy.
This is the bridge to legibility. A computer system needs the world to arrive as fields, labels, scores, documents, images, prompts, clicks, and logs. Once an institution depends on those formats, people learn to become machine-readable. They optimize resumes for filters, claims for portals, identities for forms, speech for moderation systems, and work for dashboards. The map does not simply describe the person. It trains the person to survive the map.
The AI-Age Reading
Read in 2026, the book feels less like a period piece than a precondition for AI literacy. Many current AI arguments begin too late, at model capability. Broussard begins earlier, with the cultural permission structure that lets organizations reach for computation before asking whether computation is the right form of care, judgment, repair, or governance.
Large language models make technochauvinism more seductive because they move from number-shaped authority to language-shaped authority. Older automated systems often looked bureaucratic: scores, ranks, flags, eligibility notices. New systems can sound patient, nuanced, and self-aware. That does not eliminate the old risks. It makes them easier to accept.
The practical test is simple: what human capacity is the system replacing, and what institutional duty is it helping avoid? If an AI tutor supplements a teacher, the details matter. If it becomes an excuse to underfund teachers, the product has become policy. If an AI assistant helps a benefits worker find information, that is one thing. If it becomes an unappealable filter between a person and support, the interface is now an administrative authority.
Broussard's book gives readers permission to be specific. Do not ask whether AI is good or bad in the abstract. Ask whether the data fits the case, whether the objective fits the social purpose, whether affected people can contest the output, whether the tool improves institutional responsibility, and whether a nontechnical solution would work better.
Where the Book Needs Updating
The book predates ChatGPT, consumer-scale generative AI, tool-using agents, synthetic media pipelines, prompt injection, model cards as public governance artifacts, and the current compute race. It does not fully address foundation-model supply chains, large-scale data extraction, or the infrastructure politics of training and deployment.
Its examples are deliberately accessible, which is a strength for public literacy and a limit for specialist readers. People looking for detailed technical audits of transformer models, reinforcement learning, interpretability, or frontier-model risk will need other books and papers.
Still, the book's central warning survives the model shift. A society can be technically sophisticated and still be naive about institutions. It can build better models while making worse choices about where authority should live.
The Site Reading
The site-level lesson is to test every machine claim against the world it compresses.
When an institution adopts AI, the central question is not only accuracy. It is what has been made legible, what has been erased, who must adapt to the interface, who can refuse, who can appeal, what labor disappears from view, and which human obligations are being converted into technical outputs.
Recursive reality begins when a representation starts training the reality it represents. A model misreads a domain. An institution acts through that misreading. People adapt to the system's categories. Their adaptation becomes new data. The next version looks more confident because the world has been bent toward the instrument.
Broussard's contribution is a useful discipline of disenchantment. Treat computers as tools with limits, not oracles with dashboards. Demand working systems, honest evidence, responsible institutions, and the humility to leave some problems in human hands.
Sources
- MIT Press, Artificial Unintelligence: How Computers Misunderstand the World, publication details, description, and author bio.
- MIT Press Direct, Artificial Unintelligence, DOI and bibliographic record.
- PROSE Awards, 2019 Award Winners, Computing and Information Sciences category.
- MIT Press, Awards, listing for Artificial Unintelligence.
- Meredith Broussard, About, author biography and current affiliations.
- Times Higher Education, review of Artificial Unintelligence, July 19, 2018.
- Journal of Intellectual Freedom & Privacy, review of Artificial Unintelligence, 2018.
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