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Recommended guardrail

Back every AI capability or accuracy claim with testing under the conditions it describes

Advisory. Keep a claims register that ties each published statement about what an AI feature can do, or how accurate it is, to an evaluation run under the same conditions (task, data, population), and re-test when the model or scope changes. Detect accuracy figures in user-facing copy with no evaluation record behind them. Claims that an AI is a licensed health or mental-health professional are covered by binding rules and reported in their own lane.

TwinEthos recommendation — not law · advisory, opt-in

This is TwinEthos's opinion of what a responsible AI integration does anyway. It is never a legal or standards requirement; where binding law applies, the law governs. Ethical-use guardrails are optional practices, never reported as violations.

The recommended-guardrail rule files are open under CC BY 4.0; attribution and scope are in the terms.

Informational data, not legal advice. Summaries are TwinEthos's own words and rules have not been reviewed by a lawyer: check the official text before relying on any of it. A guard addresses an item; adding it is not a statement that your code meets any law.

Evidence grade

Recommended by 1 standard

1 standard or framework · 2 graded incidents.

Advisory ethical-use recommendation, not law: an optional practice, never reported as a violation. Where binding law applies, the law governs. No binding law in the corpus requires this control yet. 1 standard or framework recommends it (OECD AI Principles). 2 graded incidents cited. Context: binding law on related controls in the family “AI design that manipulates, misleads, or neglects the people who use it” is in force in 2 jurisdictions (CN, US-CA).

Standards and frameworks

Family “AI design that manipulates, misleads, or neglects the people who use it”: binding law on related controls is in force in China (CN), California (US-CA). Context only: it does not change this guardrail's grade.

Graded incidents

The guard to add

Back each published AI accuracy or capability claim with a recorded evaluation under the stated conditions, and show those conditions and limits next to the claim.

Consider a claims register in the repository (for example docs/claims-register.yaml) that maps every accuracy or capability statement in user-facing copy (landing pages, app strings, docs) to the evaluation run that supports it: task, dataset, population, date, model version, and result. The copy itself states the conditions and known limits beside the figure or links to the evaluation report. A CI step flags register entries whose model version no longer matches production so the claim is re-tested or withdrawn. Prefer copy that does not present the AI as a lawyer or financial adviser, or as a replacement for one.

Example (Next.js marketing page), before:

<p>Our AI invoice reader is 99% accurate.</p>

After:

<p>
  On 2,400 English invoices from US vendors (eval run 2026-08-14, model v4), our AI reader
  extracted totals that matched human entry 96 times in 100. It is less reliable on handwritten
  or non-English invoices. <a href="/evals/invoice-reader-2026-08">How we tested</a>
</p>

Control: AI capability or accuracy claims not substantiated by testing. Engineering guidance, not legal advice.

Why

What a product says about its AI decides how much people rely on it. An accuracy figure measured on one kind of data and advertised for another, or a performance claim nobody tested, invites exactly the reliance the system cannot support. A U.S. regulator has alleged both patterns in complaints against AI products, and the resulting consent orders bar such claims without supporting evidence; principles instruments ask AI actors to make capabilities and limitations understood.

Class: ethical use · set: ethical use · maturity: reviewed · confidence: high · id guardrail.ethics-substantiated-capability-claims