Policies in Parallel? A Comparative Study of Journalistic AI Policies in 52 Global News Organisations
Bibliographic record
Abstract
A growing number of news organisations have set up specific guidelines to govern how they use artificial intelligence (AI). This article analyses a set of 52 guidelines 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 comparative insights into how news outlets address both expectations and concerns when it comes to using AI in the news. Drawing from neo- institutional theory and the concept of institutional isomorphism, we argue that the policies show signs of homogeneity, likely explained by isomorphic dynamics which arose 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, with both accounting for some of the variation seen in the data. We conclude by pointing out blind spots around technological dependency, sustainable AI, and inequalities in current AI guidelines and providing directions for further research.
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How this classification was reachedexpand
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".