What a robustness assessment can and cannot claim
The introduction calls a robustness assessment one step toward trust in AI systems, particularly neural networks. It does not say that such an assessment establishes trust, safety or conformity.
The requirement in plain English
The introduction calls a robustness assessment one step toward trust in AI systems, particularly neural networks. It does not say that such an assessment establishes trust, safety or conformity.
What the public record says
The public text calls a robustness assessment one step toward trust, and says the document is about AI systems built around neural networks.
Why it matters to you
The difference between one key step and trust is the difference between evidence and assurance. A robustness result is evidence inside a wider argument: it does not prove safety, it does not certify, and it does not transfer to a different distribution. Buyers who repeat the stronger claim hand a reviewer an easy finding. The safe wording is concrete: what was tested, over which input region or sample, with what result, and what remains unknown.
Evidence a reviewer asks for
- A claim register with the approved wording for each robustness result.
- The limits stated in the same document as the result.
- A reviewer sign-off on the wording before it reaches a customer.
Checks to run this month
- Does any public claim say the system is robust without qualification?
- Is the limit of the result stated next to the result?
- Who approves the wording?
Dates
FDIS registered 2026-08-21.
Cross-references
- Clause 1, Scope
- Assemble the evidence pack a reviewer will test
- Robustness as a reliability sub-characteristic
What this page does not state
No assurance level, certification or trust rating is stated.