Writing an AI Policy for a Court: Approval, Disclosure, Review, and Audit

Most courts writing a court AI policy are writing it late. That is not a criticism, it is the position nearly everyone is in, and it changes what the document has to do. A policy written before adoption can be cautious and general. A policy written after adoption has to describe practice that already exists, decide which parts of it to endorse, and be specific enough that people change what they are doing.

The evidence on that timing is unambiguous. UNESCO's survey of judicial operators, feeding guidelines built on consultation across more than 160 countries and over 36,000 judicial actors, found 44 percent had used AI tools while only 9 percent said their institution had issued guidance or provided any training on them. Seventy-three percent said there should be mandatory regulations and guidelines. In the US, a Northwestern study put more than 60 percent of responding federal judges as having used an AI tool at least once.

This guide covers what a court AI policy has to contain, section by section, with the decisions that are genuinely contested flagged as such. It is written for the court general counsel, AOC CIO, or policy lead who has been handed the task.

A scope note, because these two questions get answered in one meeting and should not be. This article is about the document the institution writes. What a judge should actually reach for at the bench, and how to tell a serious tool from a demonstration, is a different question with a different reader, and it is covered in AI tools for judges.

Why a ban is not on the table

Start by ruling out the option that looks safest.

A prohibition would have to be enforced against a population that is already using these tools, largely through general-purpose products on personal accounts, in work that produces no artefact showing the tool was involved. The realistic effect of a ban is not zero use. It is unrecorded use, which is worse than governed use in every respect that matters: no review step, no disclosure, no audit trail, and no way to answer a question about it later.

The frameworks reflect this. UNESCO's guidelines are built to enable responsible adoption rather than to discourage it, structured around 15 principles covering transparency, accountability, human oversight, human rights protection, and multistakeholder governance. CEPEJ's European Ethical Charter, adopted in 2018 and still the reference text in Europe, does the same for member states.

The policy question is therefore not whether, but which tasks, under what conditions, with what disclosure.

Who approves a use case

This is the section courts most often skip, and it is the one everything else depends on.

Somebody has to be able to say yes. In practice courts choose between a standing committee, a designated officer such as the CIO or general counsel, and the presiding judge for judicial use with an administrative owner for everything else. Each works. What does not work is leaving it implicit, because the effect is that anyone who wants to use a tool decides for themselves whether it is allowed.

The policy should distinguish two categories that are usually conflated. An institutional use case is the court deploying a system: transcription of proceedings, redaction of records for release, search across the case management system. It goes through procurement, has an owner, and is configured once. An individual use is a judge or clerk reaching for a general-purpose tool to help with a specific task. Institutional use is governable through architecture. Individual use is governable only through rules and training, which is exactly where the 9 percent figure bites.

Most courts need provisions for both, and the second is harder.

Permitted, restricted, and prohibited

The most useful thing a policy can do is name tasks explicitly rather than describe categories abstractly. Staff cannot apply a principle to a novel situation reliably, but they can check a list.

A workable structure sorts tasks into three tiers:

The prohibited tier deserves reasoning in the document rather than assertion, because staff who understand why a line exists apply it to cases the list did not anticipate. The reason is not that the technology is unreliable. It is that judicial discretion is the function the institution exists to perform, and a system trained on prior decisions reproduces the patterns in them, including patterns a court would not defend if stated aloud.

The jury prohibition is worth its own sentence. Jurors decide on admitted evidence and nothing else. A tool that summarizes exhibits for them introduces material never admitted, which is a due process problem rather than a configuration choice.

Disclosure

Courts have converged on the principle and diverged on the scope. The Philippines, the first ASEAN judiciary to formally adopt the UNESCO framework, requires disclosure whenever AI is used and authorizes it only for specified tasks.

The decisions a policy has to make:

Does disclosure attach to the task or the output? Disclosing that the court uses transcription tools generally is different from noting on a specific transcript that it was machine-produced and human-reviewed. The second is more useful to a party and more onerous to administer.

Does it vary by tier? Most courts that have written this distinguish. Transcription and translation are often disclosed once, at the level of court practice. Drafting assistance is more often disclosed per matter, if at all.

Can a party object, and to what effect? If the answer is yes, the policy needs a route. If no, it should say so, because silence invites the question to be litigated.

What will the court say if asked which model was used and where it ran? A court should be able to answer this. Being unable to is itself a finding about the court's governance.

There is legislative movement here worth tracking. In the US, the Research and Oversight of AI in Courts Act of 2026, introduced in March 2026 as H.R. 7997 and S. 4154, would require the judiciary to stand up a 15-member task force on AI speech-to-text and automatic speech recognition in federal courts, reporting within 18 months. Policy written now should anticipate that transcription specifically will attract rules.

Human review and the record of it

Every framework requires human oversight. Most policies satisfy the requirement with a sentence stating that outputs are reviewed by a human, which is an assertion about oversight rather than a design for it.

A review provision that works specifies four things for each permitted or restricted task: who reviews, what they see, what they are certifying, and where that is recorded.

What the reviewer sees is the part usually left out, and it is decisive. A reviewer shown only an output has to redo the underlying work to check it, which means either the review does not really happen or the time saving that justified the tool is consumed by verifying it. A reviewer shown the output alongside its cited sources can check it in seconds. This is why citation to source is a governance requirement rather than a nice feature, and it should appear in the policy as one.

Certification matters too. A person signing off on a transcript is attesting to something different from a judge editing a draft order. The policy should say what each signature means.

For transcription specifically, the accuracy threshold question is contested and worth resolving explicitly rather than by practice. Whether AI transcription can be trusted with the official court record covers the thresholds, speaker identification, and the review step that separates a working draft from a certified record.

Logging and auditability

Assume that in three years somebody will ask how a particular output was produced, in a context where the answer matters.

The policy should require, for any AI-assisted step touching the record or evidence: what was generated, from which inputs, by which system and model version, who reviewed it, when, and what they changed. Those logs need to be tamper-resistant and retained on the same schedule as the material they describe, not on a shorter operational one.

Reproducibility is the hard part and courts should be honest about its limits in the document. If the underlying model has since been updated, the same prompt will not produce the same output. What a court can preserve is the input, the output, the citations, and the review record, which is enough to establish what happened even when it is not enough to re-run it. Explainability and audit trails for AI in courts covers what that record has to contain to survive a challenge.

Vendor conditions, and keeping the policy current

What the policy demands of vendors

A court AI policy that only governs staff behavior is doing half the job. The other half is the conditions it imposes on anything the court buys, because most of these properties cannot be added afterward.

At minimum the policy should require that procurement establish which model runs and whether it can change without notice, where processing happens and whether court content leaves the environment, whether court data trains anything, how outputs are cited, what is logged and whether the court can export those logs, whether human review is enforced by the product or left to policy, and what the court retains if the contract ends.

What courts should ask AI vendors before signing anything turns that into a list a procurement officer can put in front of a supplier, phrased so that evasive answers are visibly evasive.

Drafting these provisions for your own court? Request a demo to see how citation, review gates, model control, and audit logging work in a deployed system.

Review cadence

The tools change faster than the policy. A document with no revision mechanism becomes inaccurate within a year and is then either ignored or enforced against practice that has moved on.

Build in a scheduled review, name who owns it, and specify the triggers that force an earlier one: a new institutional use case, a change in a vendor's underlying model, new legislation or higher-court guidance, or an incident. Courts that treat the policy as a living document tend to keep it credible with the people it governs.

The tasks most courts govern first

Policy in the abstract is hard to write. In practice most courts are governing a small number of concrete workflows, and it is easier to write the document with those in view.

Transcription of proceedings. Now the most common institutional AI use in courts, driven by the collapse in stenographer numbers rather than by enthusiasm. Governance centers on accuracy thresholds, the certification step, and retention. Smaller courts often meet this first as a capacity question rather than a policy one, which is the framing in AI for municipal courts.

Redaction for release. Records requests outpace what clerks can process by hand, and the consequence of a miss is a person harmed rather than a service level breached. AI redaction for court records and public release covers where automated detection helps, where review stays human, and what the release log must show.

Anonymizing judgments before publication. In Europe this is a standing legal duty rather than a project, because judgments must be pronounced publicly under Article 6 of the European Convention on Human Rights while personal data must be minimized under the General Data Protection Regulation (GDPR). Anonymizing judgments before publication covers doing it at national volume and auditing it afterward.

Translation of decisions. Where a judgment a litigant cannot read is a judgment not really delivered. India's Supreme Court had machine-translated more than 53,000 judgments across 17 languages by 2024, on a system that supports 19. AI translation of judgments and legal documents covers what legal-domain translation requires that general translation does not, and how courts verify it.

Judicial use at the bench is a separate governance conversation with a different reader, covered in AI tools for judges.

How VIDIZMO AI Intelligence Hub fits

Most of a court AI policy is a governance document, and no product writes it. What a platform can do is make specific provisions enforceable rather than aspirational.

Three properties matter for policy compliance. Source citation on every answer, tied back to document, page, or timestamp, is what makes the human review provision workable rather than nominal. Customer-controlled models and deployment inside the court's own environment, including on-premises and air-gapped configurations, is what lets a court answer the residency and model-control questions its own policy requires it to ask. And human-review and approval gates configured into the processing workflow are what turn a review requirement into a step that cannot be skipped under time pressure.

Where the platform does not help: it does not decide which tasks your jurisdiction should permit, it does not draft your disclosure rules, and it does not resolve whether a party may object. Those are court decisions, and a vendor claiming otherwise should be treated with suspicion.

The document worth writing

The gap UNESCO measured, 44 percent using these tools against 9 percent whose institution had issued guidance or training, is not going to close through caution. It closes through a written boundary that people can actually apply.

A court AI policy is worth writing badly and revising rather than writing perfectly and late. Name the tasks. Say who approves. Specify what review means for each one. Require the logging. Put the vendor conditions in procurement. Then schedule the revision, because the version you write this year will be wrong next year, and a policy that expects that is more durable than one that does not.

Request a demo to walk through how citation, review gates, model control, and audit logging support the provisions above.