Policies in Parallel? A Comparative Study of Journalistic AI Policies in 52 Global News Organisations
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
A growing number of news organisations have set up guidelines to govern how they use artificial intelligence (AI). This article analyses a set of 52 guidelines, mainly from Western Europe and North America, from publishers in Belgium, Brazil, Canada, Finland, Germany, India, the Netherlands, Norway, Sweden, Switzerland, the United Kingdom, and the United States. Looking at both formal and thematic characteristics, we provide insights into how publishers address expectations and concerns around AI in the news. Drawing from neo-institutional theory and institutional isomorphism, we argue that the policies show signs of homogeneity, likely explained by isomorphic dynamics arising as a response to the uncertainty created by the rise of generative AI after the release of ChatGPT in November 2022. Our study shows that publishers have already begun to converge in their guidelines on key points such as transparency and human supervision when dealing with AI-generated content. However, we argue that national and organisational idiosyncrasies continue to matter in shaping publishers’ practices. We conclude by pointing out blind spots around technological dependency, sustainable AI, and inequalities in AI guidelines and providing directions for further research.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it