Concern 223 · Working draft · editorial review in progress
Assurance-token & attestation schemes
Assurance tokens and attestations are signed claims about a system or its evaluation; the concern is mistaking a valid signature for proof that the system is safe.
Understanding the concern
Cryptography can help show who issued a statement and whether it was altered. An attestation might identify a software version or a test result. Those properties can make evidence easier to verify, but they do not decide whether the test was adequate or the claim was true.
A meaningful assurance statement needs scope, date, issuer, evidence, and a clear connection to the system being used. It can become stale after changes. The verifier must still judge the issuer and the evaluation, rather than treating a technical badge as a complete answer.
An example
A sealed inspection certificate is authentic, but it may cover an old version or only one part of the machine.
Illustrative unless identified as a documented case.
Words in plain English
- Token
- A small unit of text processed by a language model, such as a word or part of a word.
Evidence and its limits
RFC 9334 defines evidence and appraisal roles; applying them to AI evaluation requires additional choices about what the evidence establishes.
Internet Engineering Task Force · 2023 · Technical architecture standard
RFC 9334: Remote ATtestation procedureS Architecture ↗ (opens in a new tab)Defines evidence, verifier and appraisal roles; an authentic attestation does not establish that an AI evaluation is adequate.
UK Department for Science, Innovation and Technology · September 2025 · Official policy roadmap
Trusted third-party AI assurance roadmap ↗ (opens in a new tab)Proposes professional and market-quality development; a roadmap is not evidence that every service already meets a common standard.
Broader frameworks and background
These resources provide context. They do not establish every claim in this concern.
A question to discuss
What exactly is being attested, by whom, and does it still describe the system in use?
Keep track of different views, what needs checking, and any next step in your own discussion notes.
Open discussion notes →Principles for the conversation
Before you finish · reflect together
Virtues and principles to consider
Virtues are qualities we practice in how we treat people and make choices. They are not scores for judging someone. Use these ideas to explore what a thoughtful response could look like.
Trustworthiness
Trustworthiness means giving people good reasons to rely on you. It grows when promises, actions, and evidence line up. With AI, a friendly voice or a famous brand is not enough: people need to know what was checked and what could still go wrong.
In practice, keep promises small enough to test, record mistakes, and tell people when the tool's limits affect them. If a service promises human review, make sure a person really has time and authority to review it.
Ask together: What would give the people affected a sound reason to trust this use?
Accountability
Accountability means being answerable for decisions and taking responsibility for repair. It requires more than saying that the software made the choice.
Name the person or team responsible, keep useful records, and give them authority to correct problems. Make the route to a complaint clear to the people affected.
Ask together: Who will answer for the outcome and help put things right?
Truthfulness
Truthfulness means making claims that match the evidence and being clear about what we do not know. It includes correcting mistakes rather than protecting a polished story.
Check sources, label guesses, and distinguish a trial result from a proven benefit. If AI helped produce a claim, someone still needs to take responsibility for checking it.
Ask together: What can we honestly say, and what still needs checking?
Editorial ratings
These are editorial judgments from the register, not measured probabilities or predictions about your situation.
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