Thanks to my amazing coauthors
@markar.bsky.social, Destiny Akinode, @kthai1618.bsky.social, Bradley Emi, Max Spero and @miyyer.bsky.social and the support of UMD Clip lab and Pangram Labs

We will be continuously monitoring American news to keep up with how AI use changes over time. Follow along at 🌐 ainewsaudit.github.io

We’re releasing:
🌐 Browse articles: ainewsaudit.github.io
📂 Datasets (recent_news, opinions, ai_reporters): github.com/jenna-russe...
📄 Paper: arxiv.org/abs/2510.18774

AI has been creeping into the news all of us read, often without any disclosure. We call for clearly defined standards for U.S. newsrooms:
1️⃣ Clearly define what counts as acceptable use of AI and publish these standards openly
2️⃣ Require AI-use attestations for all writers

Many AI-written stories still contain authentic quotes. We hypothesize that people often use AI for editing or expanding on their human-written work. But with no disclosure, there's no way to tell for sure.

We also track how AI adoption has evolved over time:
Among 10 veteran reporters we followed longitudinally, AI use rose from 0% pre-ChatGPT (2022) to >40% in 2025.

AI is disproportionately affecting news written in languages other than English. Roughly ~8% of English news is AI-generated, compared to 33% of non-English languages (primarily Spanish). Without disclosure, we cannot be sure whether AI is translating stories or writing them.

In NYT, WaPo & WSJ, opinion sections show 6.4× higher AI use than other sections, rising ~25× since 2022 (from ~0% → ~4%).
AI use is concentrated among prominent guest authors: politicians, CEOs, and scientists.

Despite widespread use, transparency is basically nonexistent.
Out of 100 AI-flagged articles we manually annotated, only 5 disclosed that AI was used and over 90% of outlets have no public AI policy.

AI use isn’t evenly distributed:
🗞️ Far higher in small local papers than national outlets
🌎 Especially common in Mid-Atlantic & Southern states
🏢 Largely Driven by ownership groups (e.g. Boone Newsmedia & Advance Publications)
🧭 Most concentrated in weather, tech, and health

We detect AI using Pangram, a model with a reported false positive rate of 0.001% on news text. We find that 5.2% of recent news Is completely AI-generated, with another 3.9% partially AI-generated. www.pangram.com/

There is a quasi-religion in Silicon Valley that views AI as godlike. This faith has always been parallel to Evangelical Christianity: salvation (transhumanism), the rapture (the technological singularity), and demons (Roko's Basilisk)

Lately the AI faith has fully fused with Christian Nationalism.

Also, the non experts have a range of LLM usage. Having a writing background is key, and a fact many are missing.

Hi Shane. We originally used 5 people, only 1 of whom could detect AI-generated text. I then searched out people who I thought could be experts and they had to pass multiple rounds of testing to be included in the study. Details in appendix. Nonexpert performance is already widely known.

This is a great question - we didn’t dive deeper than choosing articles from American publications. There were a few mentions where experts mentioned this awkward phrasing and thought it could be a non-native speaker, but still knew it was a human!

It would be very interesting to see if every language had their own set of “AI vocab” words 🤣

I think importantly is user who do writing tasks like editing/publishing! It’s the mix of having great language skills and frequent usage. Alot of ppl who just use LLMs a lot are way worse detectors than they think they’ll be.

Link found in last post of thread 😀 (but putting it here again) arxiv.org/abs/2501.15654

📎 Paper: arxiv.org/abs/2501.15654
👩‍💻 Code & Data: github.com/jenna-russe...

Thanks to my amazing coauthors @markar.bsky.social and @miyyer.bsky.social and the support of UMass NLP