A Call for Open Science in AI Safety
A group of researchers has launched a petition urging frontier AI developers to share their safety methods and research with the global community. The initiative argues that transparency is essential for independent scientific scrutiny, which helps assess and improve the safety of increasingly powerful AI systems. While acknowledging that specific exceptions may be necessary for security risks, the signatories emphasize that broad openness accelerates progress in AI safety research.
- ▪The petition calls for the sharing of detailed descriptions of safety methods, evaluations, and relevant code to enable independent reproduction and improvement.
- ▪Transparency is argued to facilitate the targeted allocation of research funds and efforts across academia and industry toward the most promising safety directions.
- ▪Signatories include prominent researchers from institutions such as EPFL, ETH Zurich, Oxford, and MIT, who initiated the call on September 18, 2026.
- ▪Exceptions to sharing information are permitted only when releasing specific data creates a credible security or misuse risk, and these exceptions should be proportionate.
- ▪The goal is to allow safety research to keep pace with rapid advances in AI capabilities by leveraging the expertise of the global scientific community.
Hacker News (AI / LLM) files mainly under ai. We currently carry 5,655 of its stories.
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Story provenance
Attribution is not the same as permission. This drawer separates discovery metadata, excerpts, WeSearch-generated summaries, reuse status, and whether the publisher receives the visit. Nothing here claims a legal grant the publisher has not made.
Record
| Original publisher | Github |
| Canonical URL | https://make-safety-open.github.io/ |
| Publication time | Sat, 19 Sep 2026 21:56:00 +0000 |
| Retrieval time | 2026-09-19T22:03:46.812Z |
| Last seen | 2026-09-19T22:03:46.812Z |
| Headline source | Publisher (no WeSearch rewrite) |
| Excerpt source | publisher body |
| Excerpt method | First ~120 words (~800 chars) of extracted publisher body, fair-use limited. |
| Summary | WeSearch · cerebras-chat (WeSearch summarizer) |
| Summary source text | contentText |
| Citation coverage | Summary is a WeSearch-generated derivative; primary citation is the original publisher URL. |
| Cluster | fZVTmNXRBX2g · 1 stories |
| Cluster logic | Grouped by semantic title/content similarity across sources within a rolling window. Same-publisher template collisions are excluded from coverage comparison. |
| Ranking reason | Story pages are not engagement-ranked. Hub feeds use recency, with optional source-diversified chronological ordering (cap consecutive stories per source). No personalized ranking. |
| Publisher visit | Yes — open original |
| Substitutes article? | No — link-out required for full text |
Rights status (four layers)
WeSearch handling by dimension
| Indexing | May the item be indexed (stored, ranked, made findable)? | Allowed |
| Snippet | May a short excerpt of the publisher's text be shown? | Allowed |
| AI summary | May WeSearch generate its own short summary of the article? | Limited |
| Retrieval / RAG | May the content be exposed for third-party retrieval-augmented generation? | Not asserted |
| Model training | May the content be used to train AI models? | Not asserted |
| Commercial reuse | May the content be reused commercially? | Not permitted |
Basis: Derived from the published RSS/Atom feed. Contact: [email protected]. Reviewed: 2026-07-24.
Opening excerpt (first ~120 words) tap to expand
A Call for Open Science in AI Safety Sign the petition We call on frontier model developers to share their AI safety methods and research with the global community, to enable independent scientific scrutiny and advancement. The safety of increasingly powerful AI systems should be documented with detailed descriptions of methods and evaluations that independent researchers can examine, challenge, reproduce and improve. Transparency will make a big difference for both assessing and improving the safety of AI models. Assessing AI safety will be facilitated by sharing sufficient detail about the safety-relevant aspects of each frontier model—including evaluations, safety-training recipes, relevant code and data, and evidence of desired and undesired behavior.
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Excerpt limited to ~120 words for fair-use compliance. The full article is at Github.
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