AI use in American newspapers is widespread, uneven, and rarely disclosed
Not on the map. Why?
Abstract (arXiv)
AI is rapidly transforming journalism, but the extent of its use in published newspaper articles remains unclear. We address this gap by auditing a large-scale dataset of 186K articles from online editions of 1.5K American newspapers published in the summer of 2025. Using Pangram, a state-of-the-art AI detector, we discover that approximately 9% of newly-published articles are either partially or fully AI-generated. This AI use is unevenly distributed, appearing more frequently in smaller, local outlets, in specific topics such as weather and technology, and within certain ownership groups. We also analyze 45K opinion pieces from Washington Post, New York Times, and Wall Street Journal, finding that they are 6.4 times more likely to contain AI-generated content than news articles from the same publications, with many AI-flagged op-eds authored by prominent public figures. Despite this prevalence, we find that AI use is rarely disclosed: a manual audit of 100 AI-flagged articles found only five disclosures of AI use. Overall, our audit highlights the immediate need for greater transparency and updated editorial standards regarding the use of AI in journalism to maintain public trust.
Key facts. Cited by 10 papers in the map (data through 14 Oct 2026). Builds on 22 of its 69 references that are in the map. First posted on arXiv 21 Oct 2025.
Builds on
22 of 69 references.
References · 22
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10 more
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Built on by
Citing papers per year: 2026: 10.
10 papers.
- 2026The Impact of AI-Generated Text on the Internetcited by 6
- 2026Pangram 4 Technical Reportcited by 3
- 2026Generative AI floods and dilutes the market for bookscited by 2
- 2026Evaluating Commercial AI Chatbots as News Intermediariescited by 1
- 2026Argument Collapse: LLMs Flatten Long-Form Public Debatecited by 1
- 2026Hitting a Moving Target: Test-Time Adaptation for AI Text Detection under Continual Distribution Shiftcited by 1
- 2026Value Over Language Model: Detecting Original Contribution in Writingcited by 0
- 2026MultiGhostBench: A Multilingual Benchmark for Long-Form LLM-Generated Text Attribution under Distribution Shiftscited by 0
- 2026SlopShape: Identifying AI-Generated Commercial Web Contentcited by 0
- 2026Agents Can Use Base Models to Evade AI Detectioncited by 0
- 2026Agents Can Use Base Models to Evade AI Detection25 Sep 2026
- 2026SlopShape: Identifying AI-Generated Commercial Web Content14 Sep 2026
- 2026MultiGhostBench: A Multilingual Benchmark for Long-Form LLM-Generated Text Attribution under Distribution Shifts2 Sep 2026
- 2026Value Over Language Model: Detecting Original Contribution in Writing1 Sep 2026
- 2026Pangram 4 Technical Report29 Jul 2026
- 2026Generative AI floods and dilutes the market for books22 Jul 2026
- 2026Hitting a Moving Target: Test-Time Adaptation for AI Text Detection under Continual Distribution Shift23 Jun 2026
- 2026Argument Collapse: LLMs Flatten Long-Form Public Debate1 Jun 2026
- 2026Evaluating Commercial AI Chatbots as News Intermediaries21 May 2026
- 2026The Impact of AI-Generated Text on the Internet14 Apr 2026