Fact-checking Policy

Our verification process ensures accuracy across research claims, product announcements, and policy updates.

Verification First

We verify claims against primary sources (papers, repos, model cards, release notes), public benchmarks, and official statements before publication.

Our Fact-checking Process

1

Primary-Source Verification

Trace information to original papers, documentation, model cards, release notes, or code repositories.

2

Benchmark & Metric Checks

Validate reported scores on public leaderboards or reproduce claims when feasible; note evaluation caveats.

3

Expert Consultation

Consult researchers, ML engineers, or domain experts for complex technical assertions or safety claims.

4

Policy & Legal Confirmation

Confirm policy/regulatory details from official government or agency publications and legal filings.

5

Final Editorial Review

Ensure accuracy, context, sourcing, and clear labeling of analysis vs. reporting.

Types of Information We Verify

Performance and Benchmark Data

  • Model benchmarks and evaluation protocols
  • Dataset composition or licensing
  • Training compute, token counts, and hardware when disclosed
  • Fine-tuning, safety, and alignment methods

Technical Information

  • Algorithmic techniques and architectures
  • Implementation details from repos or docs
  • Model card disclosures and known limitations
  • Versioning, release notes, and changelogs

Regulatory and Policy Information

  • Government or regulator announcements
  • Standards and compliance guidance
  • Legal developments and court decisions
  • Risk management and reporting requirements

Company and Research Lab Claims

  • Partnerships and funding announcements
  • Roadmaps, milestones, and product launches
  • Authorship and affiliation details
  • Security, misuse, or safety incident disclosures

Community-sourced Information

Reddit and Social Media

Community discussion informs story selection and context but is not treated as a primary source without corroboration.

Our Approach:

  • Clearly label community insights and sentiment
  • Independently verify technical claims and metrics
  • Distinguish speculation/opinion from factual reporting
  • Attribute posts and provide context for readers

Quality Standards

What We Verify

  • All numerical claims and metrics
  • Direct quotes and attributions
  • Technical specifications and limitations
  • Chronology and timeline accuracy
  • Regulatory status and compliance details

What We Label

  • Speculation and analysis (clearly marked)
  • Preliminary or non-replicated research
  • Developing stories with clear update notes
  • Potential conflicts of interest
  • Vendor-provided demos or claims

Reader Feedback on Accuracy

Help us improve. If you spot an error or have additional evidence, email:

Email: editorial@aiconnectnews.com

All accuracy concerns are reviewed within 24 hours.

Last Updated: August 2025