Proof, attribution, and the work somebody else can check
Mathematicians have published a declaration on what AI must not be allowed to erode in their discipline. The three values they name translate almost directly into what an enterprise should require of any deliverable produced with a model.
In June 2026 a group of mathematicians published the Leiden Declaration on Artificial Intelligence and Mathematics, after a workshop at Leiden University and with the endorsement of the International Mathematical Union. It is a short document and it is not a ban. It argues that the discipline’s core values — proof, attribution, and the pursuit of understanding rather than merely of results — are under pressure from tools that can produce a correct-looking answer without producing any of the three.
The reason to read it outside mathematics is that mathematics is the discipline with the strongest available notion of a verified result, and it is finding that notion difficult to defend. Every other field is in a weaker position, including the ones where a produced answer becomes a decision about a person.
The claim: the three values in the declaration are exactly the three properties an enterprise deliverable loses first when it is produced with a model, and each of them can be required contractually.
The translation
| In mathematics | In work you commission or produce |
|---|---|
| Proof — the result can be verified independently, step by step | Every claim traces to a source a reader can open, not to a summary of one |
| Attribution — who did what is recorded | Which parts were produced by a model, which by a person, and who checked them |
| Understanding — the result is not merely correct, it is explicable | Somebody in the organisation can defend the reasoning without re-running the tool |
The second row is the one that has commercial consequence now. A supplier producing analysis, drafting or research with a model is not doing anything improper, and most of them are. What matters is whether the deliverable says so, and whether the checking that was done is described rather than implied. Those are answerable questions and they belong in a statement of work.
The third row is the one that produces harm quietly. A deliverable nobody in the organisation can explain is a deliverable nobody can defend when it is challenged — by a regulator, by a customer, by a court, or by the person it was a decision about. The tool was fast. The explanation is the deliverable.
What to ask for
The fifth question is the one that catches the most. A generated figure is the single most dangerous artefact in an AI-assisted document, because it has the form of evidence and none of the substance, and because it survives every subsequent review — nobody re-checks a number that looks reasonable and sits in a sentence that reads well.
That is why the rule we apply to our own writing is absolute rather than proportionate: every figure traces to something countable, or the sentence is written without it. It is not a difficult rule to follow. It is a difficult rule to follow after the draft exists, which is the actual reason so much published analysis fails it.
Why this matters more in an enterprise than in a journal
Mathematics has an unusual advantage: a proof can, in principle, be checked by anyone with the time. Most enterprise work cannot. A market sizing, a risk assessment, a technical option analysis — these are read by people who do not have the source material, cannot rerun the reasoning, and are making a decision on the strength of the document’s coherence.
Coherence is the one thing generative tools produce reliably. Which means the signal that a reader has historically used to judge quality has been decoupled from the property they were using it to judge, and the only remedy available is explicit provenance. Not because anyone doubts the author’s integrity, but because the reader can no longer infer it from the prose.
What this does not tell you
The declaration is a statement of values by a professional community. It is not regulation, it binds nobody, and treating it as though it created an obligation would misrepresent it.
Nor is any of this an argument against producing work with models. We produce work with them, including research and drafting, and we think an organisation that forbids it will simply not know where it is happening. The argument is for disclosure and for checkability — that the use is stated, the checking is described, and the figures are sourced. Those are the conditions under which a tool improves a deliverable rather than quietly degrading the thing the deliverable was for.
The reader who should act is whoever signs off external work. Add the provenance questions to the acceptance criteria. Suppliers who are doing this carefully will answer in a paragraph, and the ones who cannot answer at all have told you something you needed to know before the report reached your board.