Editorial Principles
Accurate, independent AI journalism that serves the global community with integrity and transparency.
Our Core Editorial Principles
Accuracy First
We prioritize factual accuracy over speed. Claims are verified via primary sourcesâpapers, repos, benchmarks, and official statements.
Transparency
We cite sources, link artifacts (papers, code, datasets), disclose conflicts, and explain our methodology.
Community Focus
We surface reliable insights from practitioners, researchers, and builders to serve the AI communityâs need for context.
Editorial Independence
Coverage decisions are made independently of commercial, vendor, or political influence.
Content Standards
Source Attribution
- Information is attributed to specific, verifiable sources (papers, repos, model cards, release notes)
- We link directly to original artifacts whenever possible
- Anonymous sources are rare and clearly contextualized
- Community discussions are identified, quoted, and contextualized
Fact-checking Process
- Primary-source verification (paper, code, dataset, official blog)
- Benchmark cross-checks where available (e.g., public leaderboards)
- Expert consultation for technical claims or safety assertions
- Timely updates as research or product details evolve
Balance and Context
- Multiple perspectives for contested claims (researchers, industry, policy)
- Historical context on approaches, baselines, and trade-offs
- Speculation clearly labeled as analysis or opinion
- Limitations, risks, and deployment caveats are included where relevant
Error Handling
When errors occur, we correct them promptly and transparently. See ourCorrections Policy.
Editorial Feedback
We welcome feedback on standards and coverage. Contact our editorial team:
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