More than a Glitch and the Systemic Bias Machine
Meredith Broussard's More than a Glitch: Confronting Race, Gender, and Ability Bias in Tech is a direct challenge to one of the laziest excuses in computing: that discriminatory technology is merely broken technology. The book's value is its insistence that race, gender, disability, classification, data, and institutional power enter the system before anyone opens a bug tracker. A harmful output may look like an error at the interface, but the deeper problem is often a working machine built around the wrong assumptions.
The Book
More than a Glitch was published by MIT Press in 2023, with a paperback in 2024. MIT Press lists the hardcover, ebook, and paperback at 248 pages, with the hardcover published March 14, 2023 and the paperback published April 2, 2024. The publisher also lists the book as a 2023 getAbstract International Book Award winner for Business Impact, a 2024 PROSE Award finalist in Popular Science and Mathematics, and a Financial Times Best Summer Books of 2023 technology selection.
Broussard is a data journalist and professor at New York University's Arthur L. Carter Journalism Institute, and research director at the NYU Alliance for Public Interest Technology. The book follows her earlier Artificial Unintelligence, but it narrows the attack. Where the earlier book named technochauvinism as the assumption that computational solutions are superior, More than a Glitch asks what happens when that assumption is carried into systems that classify bodies, allocate opportunity, interpret health, grade students, police neighborhoods, and define default users.
The book is structured as a broad public-interest technology argument rather than a single-sector case study. Sources reviewing the book identify its recurring cases as machine learning, facial recognition, criminal justice, algorithmic grading, disability and accessibility, gender, racism, medicine, algorithmic auditing, and policy responses. That breadth is the point: the glitch frame fails because the same pattern recurs across domains.
The Glitch Excuse
A glitch is a temporary failure inside a system that otherwise deserves confidence. It suggests that the architecture is sound, the harm is accidental, and the fix is local. Broussard's title refuses that comfort. Many biased technologies do not fail because one line of code slipped. They fail because the system inherits an already unequal world, compresses it into data, and then gives the compressed version operational authority.
This is why the book belongs beside Algorithms of Oppression, Weapons of Math Destruction, Race After Technology, and Unmasking AI. All four books attack the same basic alibi: that computation is neutral until a biased user misuses it. Broussard's contribution is to make the alibi unusually practical. She does not let the reader hide inside either math or moral outrage. The question is always: what was built, for whom, with whose categories, against whose interests, under what institutional authority?
The glitch excuse is especially dangerous in AI governance because it preserves deployment momentum. If the problem is framed as a bug, the system can stay in place while engineers patch around the complaint. If the problem is structural, the harder options come into view: stop using the system, change the decision process, narrow the domain, add appeal rights, change data collection, redesign categories, or admit that a task should not be automated.
Categories Become Machinery
The book's strongest AI-era lesson is that categories are not harmless labels attached after the real technical work is done. They are part of the machinery. A database field for gender, a disability accommodation workflow, a skin-tone distribution in training data, a medical-risk proxy, a credit feature, or a classroom metric can decide what the model can see before the model begins to learn.
That makes bias more durable than a bad prediction. A bad prediction can be corrected. A bad category can keep producing error while appearing orderly. It can tell institutions who counts as normal, who is an edge case, who must appeal, who must produce extra evidence, and who disappears from the measurable world. Once those categories are embedded in procurement, dashboards, APIs, training data, and evaluation metrics, they become harder to contest than ordinary human prejudice because they arrive as infrastructure.
For AI systems, this is a legibility problem. Institutions want people to become machine-readable. But the template for readability is not neutral. It often reflects the people who had the power to define the form, the benchmark, the default body, the default name, the default face, the default speech, the default family, and the default life path. The model then returns that narrow picture as if it had discovered reality.
The Institution in the Model
Broussard's framing also prevents an overly model-centered reading of AI harm. The model is rarely the whole system. A biased face-recognition result matters because a police department, border agency, school, platform, landlord, employer, hospital, or insurer can act on it. A medical algorithm matters because clinicians, billing systems, hospital policy, vendors, and liability fears translate its score into care. A grading system matters because administrators need scalable judgment and students have weak power to contest it.
This is the connection to the site's recurring concern with automated authority. A model becomes powerful when an institution uses it to replace, compress, or pre-structure judgment. The danger is not only that a system is wrong. It is that an organization accepts the system's wrongness as normal paperwork: a score, a flag, a denial, a case note, a risk level, a confidence interval, a ticket closure.
That also explains why inclusion alone is not enough. More diverse teams, better datasets, and broader usability work can matter. But a decision system can become more inclusive while still routing people through an unjust process. A fairer classifier attached to an unfair institution may simply distribute harm with better optics. Broussard's book keeps pushing the reader back to purpose: why does this automated decision exist, and who gains authority when it works?
Ability Bias and Access
The book is particularly useful because it keeps disability in the foreground rather than treating accessibility as an afterthought. Ability bias is not just a missing compliance checklist. It is a design imagination problem. Systems assume bodies, senses, time, attention, mobility, speech, gesture, and cognition. Those assumptions decide who can pass a verification screen, use a workplace tool, receive a service, appeal a decision, or be recognized as a legitimate user.
AI makes that more important, not less. Voice systems, proctoring tools, hiring assessments, identity checks, workplace analytics, classroom software, companion bots, care robots, and medical triage tools all encode expectations about how a person should look, speak, move, respond, and explain themselves. When the interface becomes the gate, the interface's model of the body becomes policy.
This is a more concrete way to think about human-machine cognition. The problem is not only whether a machine understands a person. It is whether the person must reorganize their life so the machine can process them. A humane system gives people more ways to be understood. A high-control system narrows the acceptable human until the user has to perform legibility for the machine.
Recursive Reality
The most important loop is simple. Institutions collect records from an unequal world. Engineers turn those records into models. Institutions act on the models. People adapt to those actions. The adaptations produce new records. The next system treats those records as evidence. At that point, the model is not merely reflecting bias. It is helping manufacture the world that will later justify it.
That is why the book matters for generative AI and agentic systems, even when many examples come from older algorithmic decision tools. Today's answer engines, copilots, multimodal models, and agents do not merely classify people at a distance. They write summaries, draft reports, recommend decisions, fill forms, search records, speak to users, and carry institutional language from one workflow into another. If the underlying categories are biased, the system can spread the bias through fluent prose and routine action.
The loop also changes belief formation. A biased output repeated through dashboards, model summaries, case-management systems, search snippets, and official letters becomes harder to experience as a claim. It feels like the administrative world reporting on itself. That is how a technical artifact becomes common sense: it disappears into procedure.
Where the Book Needs Friction
The strength of More than a Glitch is its range and clarity. The weakness is the same. Readers looking for a deep technical audit manual, a detailed procurement playbook, or a full legal theory of algorithmic discrimination will need other books and policy materials beside it. Broussard gives a broad diagnostic and reform argument; she does not exhaust every domain she enters.
There is also a risk that the anti-glitch frame can flatten different failure modes. Some harms come from explicit exclusion, some from weak measurement, some from bad proxies, some from inaccessible design, some from historical data, some from vendor secrecy, some from institutional incentives, and some from using an otherwise accurate system for a task that should not be automated. The practical work is to preserve those distinctions after accepting the larger point that bias is not accidental noise.
The book is best read as an inspection habit. When a system harms people unevenly, do not start by asking how to patch the output. Start by asking what social world the system assumes, what institution it serves, what categories it hardens, and whether automation has made the wrong thing easier.
What This Changes
More than a Glitch changes the burden of proof. A developer, vendor, or agency should not be able to say that a system is neutral until critics prove bias. The stronger presumption is that every consequential system carries assumptions about race, gender, disability, class, geography, language, documentation, and bodily presentation. Governance begins by making those assumptions inspectable before deployment, not after harm becomes public.
The review standard follows from that. Ask who defined the task, who is represented in the data, who is missing, who can appeal, who audits the system, what remedies exist, what uses are prohibited, whether the interface works for disabled users, whether a true output can still be unjust, and whether a false output can become institutional fact before anyone can contest it.
The book's durable lesson is that the machine is not separate from the world it measures. It is a social instrument that can turn old exclusions into new infrastructure. Calling the result a glitch is a way of keeping the machine innocent. Broussard's answer is cleaner: inspect the system, inspect the institution, and be willing to turn it off.
Sources
- MIT Press, More than a Glitch, publisher record for title, subtitle, formats, ISBNs, publication dates, page count, award notes, summary, and author biography, reviewed June 15, 2026.
- Meredith Broussard, official author site, author role, affiliations, and book listing, reviewed June 15, 2026.
- Fabian Lütz, review of More than a Glitch, LSE Review of Books, December 28, 2023, reviewed June 15, 2026.
- Melike Asli Sim, review of More Than a Glitch: Confronting Race, Gender, and Ability Bias in Tech, International Journal of Communication 18, 2024, reviewed June 15, 2026.
- New Books Network, Meredith Broussard, More than a Glitch, episode page and summary, May 18, 2023, reviewed June 15, 2026.
- Association of American Publishers, 2024 PROSE Awards finalists and category winners, Popular Science and Mathematics finalist listing, reviewed June 15, 2026.
Book links are paid affiliate links. As an Amazon Associate I earn from qualifying purchases.