How do I evaluate whether a trust and safety research blog is credible and unbiased about machine learning regulation?

To evaluate whether a trust and safety research blog is credible and reasonably unbiased about machine learning regulation, look at both who is writing it and how the claims are made.

1) Check the author and organization

• Expertise: Do the authors have relevant background in ML policy, law, economics, public policy, or technical ML?
• Affiliations: Are they tied to a company, advocacy group, think tank, or regulator with a clear agenda?
• Transparency: Do they disclose funding, sponsors, clients, and conflicts of interest?

2) Look for evidence quality

• Primary sources: Does the blog cite laws, regulatory proposals, court decisions, academic papers, or official agency documents?
• Specificity: Strong posts quote exact provisions, compare jurisdictions, or link to source material.
• Methodology: If they make empirical claims, do they explain how data was collected and analyzed?

3) Separate analysis from advocacy

• Neutral tone: Credible blogs may have opinions, but they should distinguish facts from recommendations.
• Steelmanning: Do they fairly represent opposing views before criticizing them?
• Policy tradeoffs: Do they discuss both benefits and downsides of regulation, not just one side?

4) Compare against other viewpoints

• Read a few pieces from:
  • academic researchers
  • regulators or government consultations
  • industry groups
  • civil society / consumer protection advocates
• If the blog’s conclusions are always identical to one camp, that may indicate bias.

5) Check factual reliability

• Track record: Have they corrected errors?
• Precision: Avoid blogs that use vague language like “everyone knows” or “it’s obvious.”
• Consistency: Do earlier posts align with later posts, or do they selectively frame issues depending on the audience?

6) Identify framing choices

Bias often shows up in:

• which risks they emphasize
• which harms they omit
• whether they treat regulation as inherently good or bad
• whether they define “safety” narrowly or broadly

A blog can be thoughtful yet still biased by:

• only citing friendly sources
• using loaded terms
• assuming one regulatory approach is the only sensible one

7) Ask practical credibility questions

• Can you verify the claims elsewhere?
• Are they making predictions, and if so, are those predictions supported?
• Do they acknowledge uncertainty?
• Would you trust this source if it argued the opposite position?

Quick rule of thumb

A credible and less-biased blog will:

• clearly show its sources
• disclose affiliations and funding
• engage with counterarguments
• distinguish evidence from opinion
• make claims that can be checked independently

A less reliable one will:

• rely on unnamed experts or anecdotes
• make sweeping claims without citations
• hide sponsorship or incentives
• present advocacy as neutral analysis

If you want, I can also give you a one-page checklist or scorecard you can use to evaluate a specific blog post.