“One of Silicon Valley’s Most Divisive Topics”: How the Media Discusses Openness in AI
Bibliographic record
Abstract
In the last few years, there has been a rapid release of highly capable Artificial Intelligence (AI) systems labeled as “open” or “open source.” Openness in AI encompasses a wider range of practices than open source software: from AI systems with all components published publicly to those that only share model weights and prohibit certain uses. Today, embracing this concept is a contentious topic. Some argue that the benefits of openness outweigh its risks. Others believe open release of AI poses more risks to society. This “Open vs. Closed AI” debate has made headlines in major news outlets and has become a political topic. In this study, we analyze how AI openness is framed in the media through a qualitative analysis of 223 newspaper articles from the U.S., France, and China. We find that effective communication around this debate is hindered by inaccurate use of terminology, the presence of misleading information, and an either-or dichotomy of AI openness that does not reflect the complexity of AI system development. Our analysis also reveals significant heterogeneity across news sources, and that media discourse on AI openness focuses solely on a handful of models. The media can influence public opinion on AI, promote user adoption of certain models, and subsequently impact technology policy decisions. Therefore, we call on the AI community to help add clarity to the debate.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.013 | 0.040 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.010 | 0.027 |
| Scholarly communication | 0.017 | 0.021 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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 source (direct Gemma or distilled Codex), 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".