AI use in American newspapers is widespread, uneven, and rarely disclosed

2025-10-21

AI Summary

This study addresses the widespread, uneven, and undisclosed deployment of AI in journalism. We conduct the first large-scale empirical audit of 186,000 publicly available news articles from 1,500 U.S. newspapers. Using the Pangram detection tool augmented by human verification, we find that approximately 9% of newly published articles contain AI-generated content—editorials exhibit a markedly higher adoption rate (27.3%) than hard-news articles (4.3%), a 6.4-fold difference. AI usage is highly concentrated across specific media types, topics, and ownership structures; only 5% of AI-assisted articles disclose this fact. The findings reveal severe transparency deficits across the industry and provide the first systematic, full-population evidence—based on comprehensive analysis of publicly accessible news content—on how AI reshapes journalistic production practices and potentially erodes public trust.

Technology Category

• Natural Language Processing: Ethics — Bias, Fairness, Transparency & Privacy
• Humans and AI: Game Design — Procedural Content Generation & Storytelling
• Application Domains: Misinformation & Fake News

Application Category

• Web Mining and Content Analysis: Web data quality in the era of algorithmically-generated content
• Economics, Online Markets and Human Computation: Trust and reliance of crowd workers and data experts on GenAI
• Responsible Web: Human-perceived consequences of algorithmic deployment on the web

Abstract

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.

Problem

• Measuring undisclosed AI-generated content in American newspaper articles
• Identifying uneven distribution of AI use across outlets and topics
• Assessing transparency gaps in AI disclosure practices in journalism

Innovation

• Audited large-scale dataset using AI detector
• Discovered uneven AI use across newspaper topics
• Found rare disclosure despite AI prevalence