Blog · Review Essay · Last reviewed June 15, 2026

The Audit Society and the Rituals of Machine Accountability

Michael Power's The Audit Society: Rituals of Verification is not a book about artificial intelligence. That is exactly why it is useful for AI governance. It explains how modern institutions learned to answer distrust with verification, uncertainty with control systems, and political pressure with auditable procedures. Read now, the book clarifies a central problem in machine accountability: the audit can expose a system, or it can become the ritual that lets the system continue.

The Book

The Audit Society: Rituals of Verification was first published by Oxford University Press in 1997 and reissued as a paperback in 1999. Oxford Academic lists the paperback ISBN as 9780198296034 and the hardback ISBN as 9780198289470. Google Books and the Internet Archive bibliographic record list the original edition at 183 pages, with subjects including auditing, responsibility, management, and organizational effectiveness.

Power was writing about the spread of audit beyond financial accounting: medical audits, technology audits, environmental audits, value-for-money audits, quality audits, teaching audits, and many other forms of checking. The question was not simply why accountants had become powerful. It was why so many domains began to treat formal scrutiny as the answer to public mistrust, managerial uncertainty, and demands for accountability.

The author is a professor of accounting at the London School of Economics. LSE's profile identifies his research interests as auditability, transparency, accountability, internal control, risk management, regulation, and standardization. That institutional location matters. The book is not a generic complaint about bureaucracy. It comes from inside the study of accounting and governance, where the attraction of verification is understood as well as its dysfunctions.

Auditability Before Accountability

The book's central move is to separate accountability from auditability. Accountability is a political and organizational relation: someone can be called to answer, evidence can matter, and consequences can follow. Auditability is a design condition: the activity has been made inspectable in the particular way an audit can see.

That difference is easy to miss. Once an institution wants an audit, the institution often begins reshaping itself around what can be audited. Work becomes documentation. Judgment becomes procedure. Professional discretion becomes a checklist. Quality becomes evidence of quality. A messy public purpose becomes a set of indicators, controls, and files that can survive inspection.

This is the bridge to The Tyranny of Metrics, Trust in Numbers, and Seeing Like a State. In each case, institutions simplify the world to govern it. Power's specific contribution is to show how the simplification can be demanded in the name of accountability while quietly replacing accountability with a performance of being checkable.

A process can become highly auditable without becoming just, wise, or repairable. The record may be complete while the decision remains wrong. The control may exist while nobody is empowered to halt the system. The certification may be current while affected people have no practical way to appeal. Auditability is a necessary ingredient for some forms of accountability, but it is not the same thing.

The Ritual Problem

The subtitle, Rituals of Verification, carries the book's sharpest warning. A ritual is not fake just because it is ritualized. Rituals can focus attention, allocate responsibility, preserve memory, and make institutions answerable. The problem begins when the ritual becomes self-validating. The inspection happens because inspection is expected. The report exists because the report is required. The pass mark travels farther than the evidence behind it.

Power's account helps explain why weak oversight can feel reassuring. A formal process creates the appearance of seriousness. It has roles, procedures, terms of art, independence claims, templates, signatures, and archives. That form can discipline organizations, but it can also protect them. The audit can become a shield against the very questions that produced it: Does this activity serve its public purpose? Who is harmed? Can outsiders challenge the evidence? What happens when the audit finds a serious problem?

The ritual problem is especially dangerous when the audit becomes a purchasing signal. Buyers, regulators, boards, insurers, and journalists often need a compact token of trust. A certificate, model card, system card, conformity assessment, red-team summary, SOC report, or bias-audit notice can travel as that token. The document may be useful. It may also compress a large unresolved situation into a phrase that says, in effect, reviewed.

The AI Reading

AI governance is entering its audit-society phase. Organizations are building model inventories, risk registers, evaluation suites, red-team reports, dataset documentation, post-deployment monitoring, AI management systems, conformity assessments, vendor questionnaires, and assurance programs. The movement is necessary. Powerful systems should leave records. Claims about safety, bias, privacy, security, and reliability should meet evidence.

But AI audits inherit every failure mode Power describes. The audited object can shrink to the easiest thing to test. The model can be assessed apart from the retrieval layer, interface, workflow, human fallback, vendor contract, deployment population, and update process. A benchmark score can stand in for situated performance. A red-team exercise can stand in for safety. A bias table can stand in for remedy. A policy can stand in for control.

The strongest AI-era use of Power is therefore practical: ask what had to be done to make the system auditable. Which parts of the work were formalized? Which failures became visible? Which failures disappeared? Who chose the scope? Who paid the auditor? Who sees the report? Which finding can stop deployment? What can an affected person do with the evidence?

An audit that cannot change action is not useless, but it is politically weak. It may produce knowledge without power. For high-impact AI, the governance question is not whether a test occurred. It is whether the test is connected to procurement limits, release gates, monitoring, incident reporting, user notice, appeal, remediation, and withdrawal.

The Institution Around the Model

Power's book also pushes AI criticism away from model isolation. A model is rarely the whole accountable object. The consequential system includes the organization that sets the goal, the data pipeline that defines the world, the interface that asks for trust, the workers who handle exceptions, the policies that narrow discretion, the buyers who accept vendor claims, and the people who must live with the result.

This matters because audits can create a false boundary around responsibility. A vendor says the model was tested. An employer says the vendor supplied the tool. A regulator says disclosures were posted. A manager says the system only recommended. A human reviewer says the interface gave them the options. Everyone can point to a controlled fragment while the institutional chain remains hard to contest.

A serious audit should widen the frame. In hiring, it should include job ads, applicant pools, screening thresholds, accommodation procedures, recruiter discretion, appeal channels, and downstream outcomes. In welfare, it should include eligibility rules, documentation burdens, caseworker workloads, notice language, and hearing rights. In clinical AI, it should include workflow, liability pressure, patient communication, billing, and the medical record. In each case, model performance is only one part of institutional accountability.

This is where audit joins the broader problem of legibility. Making a system inspectable can improve governance, but inspection has a point of view. The audit sees what its method is built to see. If the method sees controls and not people, documents and not remedies, averages and not edge cases, then the audit can make the institution more confident while leaving the governed person less able to answer back.

From Audit Trails to Data Traces

Power later extended the audit-society thesis into the language of traces and traceability. In his 2022 article "Theorizing the Economy of Traces," he connects the older world of audit trails to platformization, surveillance capitalism, data-driven subject formation, and the possibility that auditing itself becomes increasingly shaped by data architectures. That later work makes the 1997 book feel less like a period diagnosis and more like a prehistory of the present.

The audit trail used to be a record that allowed a transaction or decision to be reconstructed. In the platform world, traces are not just after-the-fact evidence. They are raw material. Clicks, prompts, location pings, work logs, ratings, keystrokes, message metadata, biometrics, tickets, commits, and model interactions become the material from which organizations classify, predict, route, and optimize behavior.

This changes the meaning of accountability. The same traces that make a system auditable can also make a person governable. Logging can support appeal, debugging, security, and public oversight. It can also support surveillance, productivity scoring, suspicion, behavioral prediction, and automated discipline. The difference is not in traceability alone. It is in who controls the trace, what it can be used for, how long it persists, and whether the person recorded by it has any right to inspect, correct, or refuse it.

AI intensifies this ambiguity. Every prompt can become training residue, product telemetry, security evidence, compliance artifact, workplace record, or behavioral signal. Every model output can become a draft, a decision support artifact, a case note, a source of future retrieval, or a benchmark example. The audit society becomes recursive when the records produced for accountability become inputs to the next layer of automation.

Where the Book Needs Friction

The Audit Society is compact and powerful, but it is not a complete politics of oversight. It gives a general theory of audit expansion more than a sector-by-sector account of race, class, gender, disability, colonial administration, policing, or platform capitalism. Readers need other books beside it to understand how audit and measurement fall unevenly on different populations.

The book can also tempt an overly skeptical reading. Because audit rituals can become dysfunctional, it is easy to slide into the view that audits are merely theater. That would be a mistake. In opaque institutions, records matter. Independent testing matters. Logs matter. Public evidence matters. Affected people often need more auditability, not less, because informal discretion can be just as unaccountable as formal control.

The right conclusion is not anti-audit. It is anti-ritualized innocence. The audit should not let an organization claim virtue simply because a verification procedure occurred. It should create a harder institutional condition: evidence must remain connected to explanation, contestability, repair, and consequences.

What This Changes

Power changes the question from "Was it audited?" to "What kind of world did the audit make?" Did it make the system more answerable, or merely more documentable? Did it reveal the work, or reorganize the work around signs of compliance? Did it give affected people usable leverage, or did it let institutions exchange trust tokens among themselves?

For AI builders, this means auditability has to be designed before deployment. Preserve versioned prompts, data provenance, model changes, retrieval sources, tool calls, human overrides, incident records, and user-facing explanations. But do not confuse the existence of records with the existence of accountability. A log that nobody can use is an archive of helplessness.

For regulators and buyers, the lesson is to inspect scope and consequence. Ask whether the audit covers the deployed system, not a demonstration object. Ask whether the auditor had independence, access, and publication rights. Ask whether the report can trigger delay, withdrawal, notice, appeal, or compensation. Ask whether the people most exposed to the system can understand and use the evidence.

For critics, the book offers discipline. It is not enough to denounce compliance theater. The harder task is to specify what would make verification real. A good audit does not sanctify a machine. It gives people a way to see where authority entered the system, how evidence was produced, and what must change when the record does not support the claim.

The Audit Society remains valuable because it names a temptation that is now everywhere in AI governance: the desire to turn accountability into an artifact. The certificate, dashboard, model card, benchmark, and system report can help. They can also become the polished surface of non-accountability. The test is whether verification keeps the question open long enough for power to be challenged.

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