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The Bench & Bar AI Brief | Issue 011 | August 4, 2026

Before Court AI Earns Trust, It Needs a Test Plan

An upcoming NCSC program offers a practical starting point: define the use, the risk, the human baseline, the test, the owner, and the real cost of ongoing review.

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Tennessee Edition

Welcome to Issue 011 of The Bench & Bar AI Brief, Tennessee Edition.

Planned publication date: August 4, 2026.

The question is not whether a court can place an AI tool in front of the public. The question is whether the court can explain what the tool is for, what can go wrong, and how it will know when the answer is wrong.

That is the useful frame behind an August 19 National Center for State Courts program on evaluating and testing public-facing AI tools for courts and access-to-justice work. The program is not a rule or a Tennessee policy. It is a timely reminder that a polished demonstration is not the same thing as a tested public service.

The NCSC program identifies several parts of the evaluation problem: the tool's intended use and audience, the risk of harm from errors, relevant human baseline error rates, high-quality content, testing methods, testing volume, and the costs of content, technology, staff, contractors, and continuing review.

Those topics point to a practical truth. Accuracy is not one number that travels cleanly from one task to another. An error in a voluntary information tool may call for one response. An error that misdirects a self-represented litigant about a deadline, a required filing, or where to obtain help may call for another. The use of the tool, the audience, and the consequence of a mistake belong in the test plan before the public is asked to rely on it.

Six questions before deployment.

  1. 1What is the task? State the narrow job the tool is meant to do. “Help with court information” is too broad. “Direct a user to the correct public form page” is testable.
  2. 2Who is the audience? A tool for trained staff, lawyers, and self-represented litigants does not face the same conditions or the same risks.
  3. 3What error matters most? Define the mistake that would cause meaningful harm, not just the mistake that is easiest to count.
  4. 4What is the human comparison? A useful evaluation asks how the tool performs against an actual baseline, including the limits of human processes it may supplement.
  5. 5Who tests and corrects it? Name the content owner, the test method, the review cadence, and the person responsible for acting on a bad result.
  6. 6What will it cost to keep it trustworthy? A serious budget includes more than a software license. It includes content, testing, staffing, contractors, and continuing review.

Bench & Bar takeaway.

This is not an argument against innovation. It is an argument against asking the public to trust a system that has no defined job, no meaningful test, and no accountable owner.

For courts and law offices, the first useful AI document may not be a procurement memo or a policy statement. It may be a one-page test plan that answers these questions before a pilot grows into a public-facing service.

The question for this week: if an AI tool gave a person the wrong answer tomorrow, would your organization know how that answer was tested, who owns the correction, and what happens next?

Verified source: National Center for State Courts, “Responsible by design: Evaluating & testing AI tools for accuracy and reliability”, reviewed August 4, 2026.

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