How we read the signal

Analysis frame

Evidence level

Primary-source evidence

Analytical lens

Separate measured spending on identified AI-assisted ads from the unknown effect on voters, then test whether disclosure can make synthetic content legible at the moment of exposure.

Affected groups
  • Voters evaluating campaign imagery and claims
  • Candidates, campaigns and platforms that create or distribute AI-assisted ads
What remains unknown
  • Undetected AI use means the 220-ad count and $137 million spending estimate are lower bounds, not a full census
  • The project does not measure how many voters noticed, believed or changed their vote because of an AI-assisted ad
Second-order effects to watch
  • Campaigns may adopt routine AI production before disclosure standards settle
  • Inconsistent labels may cause voters to distrust authentic material as well as synthetic material

What the count actually covers

Wesleyan's team uses coders, disclaimers and AdImpact tags to identify AI-generated or AI-enhanced advertising. It excludes cases too uncertain to classify, so the $137 million figure is a lower-bound estimate for ads it could identify, not a census of all AI used in campaigns.

The disclosure gap

Only 27% of identified AI ads had a disclaimer. A label will not resolve whether a claim is true or fair, but it can tell a voter that a scene, voice or image may not be a documentary recording.

What this does not prove

The release says nothing about how many people were persuaded. The FEC has applied a technology-neutral fraud rule rather than a general federal AI-label requirement. The next evidence should be audited ad libraries, clear disclosures and research on what voters understood.

Primary trail

Go to the source

Read the evidence behind this analysis. External links open in a new tab.

AdImpact with analysis by the Wesleyan Media Project — October 15 election-ad release Federal Election Commission — 2024 interpretation of AI-assisted fraudulent misrepresentation