Essay

Institutional Capacity to Contest: Why an Open Window Means Little Without the Power to Act

AI will not reshape society through algorithms alone. It will depend on whether governments, schools, utilities, and other public institutions develop the capacity to understand, procure, and govern it before dependence on a handful of private actors becomes the default.

Mark Howard

— 7 min read
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The AI Concentration Window argued that the institutional order around AI is still unsettled.

The models are improving. The infrastructure is being built. Companies and governments are choosing platforms, writing contracts, and redesigning work. Many of those arrangements have not yet hardened beyond challenge.

That makes the present period a window of contestability.

But a window can be open in theory and unavailable in practice.

A local government may have legal authority to review a data-center proposal but lack the expertise to evaluate it. A worker may have a right to question an automated decision without access to a meaningful appeal. A public agency may be authorized to regulate AI while relying on the companies it oversees for technical evidence.

Nothing in those examples is formally closed.

Very little is meaningfully contestable.

By institutional capacity to contest, I mean the practical ability of public institutions and affected groups to understand how AI is being used, intervene before decisions harden, and obtain change or remedy.

Contestability is not the same as opposition. A system is not contestable only when someone can stop it.

It is contestable when affected people and institutions can shape its purposes, conditions, and distribution of risk—and when their participation can alter the outcome.

That requires more than a principle.

It requires a chain capable of turning principle into power.

From Principle to Contestability

Democratic governance often describes openness through procedure.

Was notice provided? Was public comment allowed? Is there a right to appeal?

Those questions matter. Procedural openings are better than decisions made entirely in secret.

But they can also create a reassuring picture of contestability that does not survive contact with institutional reality.

A thirty-day comment period means one thing to a company with lawyers, technical staff, and an established relationship with the regulator. It means something rather different to a school district trying to understand a new AI procurement or a small city facing an infrastructure proposal that may reshape its utility system.

Everyone may be invited into the same process. They do not arrive with the same capacity.

The 2026 Global Index on Responsible AI illustrates the gap. Across 135 countries and jurisdictions, only 27 percent had operational mechanisms for civil-society participation. Just 26 percent had frameworks for fair and accountable public procurement of AI, while 18 percent required public disclosure of government algorithmic systems. (GIRAI key findings)

Governance is arriving in layers, and the layers do not always connect.

A useful way to see the problem is as a five-step ladder:

Principle → Obligation → Institution → Operation → Contestability

A principle establishes direction: fairness, transparency, or human accountability.

An obligation turns that principle into a duty. Someone must disclose, assess, or correct.

An institution must interpret and enforce the duty.

Operation is where that institution acquires staff, evidence, routines, and technical competence.

Only then does contestability become real. Affected people can intervene, bargain, appeal, or obtain remedy.

The European Union’s AI Act shows how the first three steps can be connected. For certain deployers of high-risk systems, it requires a fundamental-rights impact assessment. Its broader high-risk framework creates duties involving risk management, documentation, and human oversight. Member states designate competent authorities to supervise compliance. (European Commission overview)

The remaining question is operational.

Can the responsible institutions hire technical staff? Can they obtain independent evidence? Can they act while choices remain open?

A right can exist without being usable. An office can exist without being capable. Participation can exist without leverage.

Rights do not enforce themselves, despite their admirable work ethic in speeches.

The ladder also shows why institutional capacity cannot live only in one central AI office.

AI enters through procurement, public services, and ordinary workplace systems. Capacity has to appear there as well.

A procurement officer must recognize lock-in. Technical staff must be able to test vendor claims. Affected people need a route into the process.

This is not glamorous work.

Institutional power rarely is.

Where the Chain Is Being Built—or Broken

Different institutions reveal different missing links.

Anticipation: the ECB and FCA

One sign of institutional capacity is the ability to act before a crisis determines the available choices.

In July 2026, the European Central Bank directed major euro-area banks to prepare action plans for AI-enabled cyber threats by October 31. It was not responding to one catastrophic attack. It was requiring supervised institutions to identify measures, assign responsibility, and prepare before emergency conditions arrived. (Reuters)

The United Kingdom’s Financial Conduct Authority took a related approach through the Mills Review. The review examined how AI could reshape consumer services, fraud, and market power. It recommended stronger coordination, expansion of the FCA’s AI Lab, and development of an AI-enabled supervisory model.

Neither action guarantees that regulators will keep pace with the market. Reviews can become shelves, and shelves have absorbed more than one administrative revolution.

But both examples show authority being converted into preparation.

That is what anticipation contributes to the ladder: time.

Participation: the Snohomish County Civic Assembly

Public concern does not automatically become public capacity.

AI decisions can be technically complex, wrapped in procurement confidentiality, and presented after substantial commitments have already been made. Ordinary public-comment processes may invite people to speak without giving them enough information or continuity to shape the decision.

The Snohomish County Civic Assembly on AI offers a different model. The county brought together randomly selected residents to learn about AI, deliberate over multiple sessions, and develop recommendations for how county government should use the technology. The assembly then delivered a final report to the County Council.

The county created time for learning, access to evidence, and a route into formal government before permanent rules were settled.

That is more than consultation. It is a small piece of civic infrastructure.

Its weakness is equally clear. If government does not respond and connect the recommendations to continuing oversight, the assembly remains an unusually thoughtful suggestion box.

The missing link would be leverage.

Implementation: Illinois schools

Governments are not only AI regulators. They are also customers, employers, and deployers.

Illinois’s 2026 statewide guidance for artificial intelligence in K–12 schools treats adoption as an institutional design problem rather than a question of whether teachers should use a chatbot. The guidance addresses governance, procurement, and professional learning. It also emphasizes defining the educational problem before selecting the tool.

A guidance document is not implementation. Four hundred pages can supply a district with help or with a new object to place beside the strategic plan.

The practical test comes next.

Do districts receive usable technical assistance? Can they evaluate vendor claims? Can less-resourced districts exercise the same judgment as wealthy ones?

Illinois has supplied a principle and a framework. The next step is to make them operational across hundreds of local institutions.

The same pattern appears elsewhere.

Workers may receive training without gaining bargaining power over how AI changes their jobs. Smaller organizations may be offered alternatives they lack the expertise to use.

In both cases, formal access exceeds practical agency.

Enabling Bureaucracy and Enclosing Bureaucracy

The word bureaucracy usually enters technology debates as an accusation.

Rules slow innovation. Agencies lack expertise. Procurement is cumbersome. Some of those criticisms are deserved.

But the absence of public bureaucracy does not produce the absence of control.

It often transfers control to private contracts, platform rules, and technical standards. These arrangements can be every bit as bureaucratic. They simply answer to different principals and are less likely to invite public challenge.

The relevant distinction is between enabling bureaucracy and enclosing bureaucracy.

Enabling bureaucracy gives many actors a stable way to act. It creates common standards, technical assistance, and reviewable decisions.

Enclosing bureaucracy uses complexity to protect the system from challenge. It privileges established participants and turns compliance into an entry barrier.

The same rule can do both.

A demanding evaluation standard may protect the public while becoming too costly for smaller firms. A centralized procurement framework may improve contract terms while locking many agencies into one vendor. A national safety process may strengthen oversight while concentrating access among a few companies and state institutions.

The choice is rarely between governance and freedom.

It is more often between visible governance and governance embedded in private infrastructure.

Strong institutions can support innovation when they make rules legible, create shared resources, and allow controlled experimentation.

The design problem is not whether institutions should be strong.

It is whether their strength remains contestable.

What Capacity Requires

Across these examples, three requirements recur.

Authority and leverage

Someone must have the standing to obtain information, set conditions, or require a remedy.

Authority can come from regulation, collective representation, or purchasing power. Without it, expertise becomes advice offered to whoever already controls the decision.

Voice without leverage may influence debate. It is less likely to change a contract.

Independent knowledge and organizational competence

Institutions need to understand the systems they govern without depending entirely on the provider’s description.

That requires access to usable evidence, technical expertise, and organizational continuity.

Transparency is not the release of more material. It is the production of information that another institution can use.

Capacity also has to survive beyond one unusually capable person. It needs routines, records, and continuity.

Time, alternatives, and remedy

Contestability is strongest before commitments harden.

Institutions need notice early enough to evaluate alternatives. They also need somewhere else to go: another provider, an interoperable system, or a different institutional arrangement.

And they need a way to correct failure after deployment through appeal, audit, or revision.

These requirements reinforce one another. Authority without knowledge can become arbitrary. Knowledge without leverage can be ignored. Alternatives without competence may exist only on paper.

Capacity is not one office, one right, or one consultation.

It is the operating system that allows all of them to matter.

The Democratic Meaning of Capacity

The concentration window is often described through assets: compute, capital, and models.

Institutional capacity belongs on that list.

A society that possesses advanced AI but lacks institutions capable of governing it has acquired capability without much agency. A government that adopts AI throughout public services but cannot evaluate vendor systems has modernized its dependency. A worker who becomes more productive without gaining information or bargaining power may receive better tools inside a more controlling organization.

Some concentration of power may be necessary to build and operate systems at AI scale.

The democratic question is whether that power remains answerable to institutions outside itself.

Can institutions see what is happening? Can affected people intervene before choices harden? Can they obtain correction or build an alternative?

These are the practical forms of contestable scale.

They are also the bridge to the next article in this series.

Institutional capacity to contest remains an abstract phrase unless it can be observed. The next paper will ask what indicators show that the window of contestability is opening, narrowing, closing, or reopening.

Three tests will matter most:

  • Visibility: Can institutions and affected people understand how AI is being used?
  • Leverage: Can they intervene before decisions harden and require review or correction?
  • Alternatives: Can they exit, change providers, or build another arrangement?

No single measure will answer the question.

A country may have strong legislation and weak implementation. A regulator may possess technical expertise but little democratic accountability. A civic process may deepen public judgment without changing policy.

The indicators will have to be read together.

But the underlying principle is now clearer.

The window of contestability does not stay open merely because alternatives remain imaginable.

It stays open when institutions have the capacity to make alternatives real.

References and Further Reading

Institutional capacity and contestability

Regulatory and public-sector capacity

Civic capacity

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