E-E-A-T says 'trustworthiness' is most important. How do you actually demonstrate trust to AI?

Trust for AI is about verifiability and consistency. Here’s the framework:

Trust Signal Categories:

1. Source Attribution

• Citations to primary sources
• Links to verifiable references
• Methodology disclosure
• “According to [Source]” statements

AI can check if your citations are real and relevant.

2. Author Transparency

• Real author names (not “Staff”)
• Verifiable credentials
• Author pages with consistent information
• Social profiles that match

AI cross-references author claims.

3. Business Legitimacy

• Contact information
• Physical address
• Privacy policy
• Terms of service
• Business registration signals

4. Content Consistency

• Claims consistent across your site
• Information matches external sources
• No contradictions within your content
• Updated, not stale

5. Technical Trust

• HTTPS (table stakes)
• No intrusive ads/popups
• Clean, professional presentation
• Fast, functional site

What breaks trust:

• Unverifiable claims
• Missing or fake author info
• Contradictions with authoritative sources
• Aggressive monetization signals
• Technical issues (security warnings, broken pages)

Let me go deep on source attribution:

What strong source citation looks like:

1. Primary Source Links Link directly to studies, not summaries of studies. “According to study title ” not “Studies show…”

2. Recency and Relevance Recent sources for recent topics. Don’t cite 2018 data for 2026 trends.

3. Authoritative Sources Government data, academic research, industry reports. Not “some blog said” or “experts say.”

4. Methodology Transparency “In a survey of 1,000 marketers by [Organization]…” Not “most marketers believe…”

What weak citation looks like:

• “Studies show…” (which studies?)
• “According to experts…” (which experts?)
• “Research indicates…” (what research?)
• Links to secondary sources that summarize primary
• Old citations for current topics

Why this matters for AI:

AI can evaluate source quality. If you cite Nature, Harvard Business Review, or government databases, that’s different from citing low-authority blogs or vague “experts say” claims.

Source quality affects your content’s trustworthiness score.

Business transparency signals that build trust:

Contact Information:

• Phone number (real, working)
• Email (real, responsive)
• Physical address
• Contact form

AI can verify these exist and match business directories.

About Us Depth:

• Company history
• Team information with photos
• Mission/values
• Credibility indicators (awards, certifications)

Policy Pages:

• Privacy policy (required for trust)
• Terms of service
• Return/refund policy (if applicable)
• Editorial standards (for content sites)

Third-Party Validation:

• BBB accreditation
• Industry certifications
• Security badges (when real)
• Review platform presence

What destroys business trust:

• No contact information
• PO Box only address
• Stock photos for “team”
• Generic or missing policies
• No third-party validation

These aren’t just for legal compliance. They’re trust signals that AI evaluates.

Content patterns that signal trust (or distrust):

Trust patterns:

1. Balanced Presentation Pros AND cons. Multiple perspectives. Nuance.

2. Limitations Acknowledged “This approach works best for X, but may not suit Y”

3. Uncertainty Admitted “Research is still emerging” when appropriate

4. Updates and Corrections “Update [date]: We previously stated X, but…”

5. Clear Disclosure “We receive affiliate commissions” when relevant

Distrust patterns:

1. Only Positive Claims Everything is the best, no downsides mentioned

2. Absolute Language “Always,” “never,” “guaranteed”

3. Hidden Commercial Intent Reviews that are actually ads

4. Manipulative Tactics Urgency, scarcity, fear without basis

5. Vague Authority Claims “Experts agree” without naming experts

AI is trained on examples of trustworthy vs. manipulative content. These patterns are learned.

YMYL (Your Money, Your Life) trust is even more critical:

For health, finance, legal content:

AI systems apply stricter trust standards because misinformation can cause real harm.

Required trust signals for YMYL:

1. Expert Authorship Content by qualified professionals (MDs for health, CPAs for finance, etc.)

2. Medical/Legal Review “Reviewed by [Name, Credentials]”

3. Sourcing to Guidelines CDC, FDA, IRS, official legal sources

4. Disclaimers “This is not medical/financial/legal advice”

5. Clear Dates Medical information especially must show currency

What happens without these:

AI systems may refuse to cite YMYL content without clear trust signals. The risk of spreading harmful misinformation is too high.

If you create YMYL content, trust signals aren’t optional. They’re prerequisites for any visibility.

This thread clarified my trust framework. Key takeaways:

Trust is Verifiable: AI cross-references claims. Fake signals get caught.

Trust Signal Categories:

1. Source Attribution

   • Real citations to primary sources
   • Methodology disclosure
   • Authoritative references

2. Author Transparency

   • Real names, verifiable credentials
   • Consistent across platforms
   • Author pages with depth

3. Business Legitimacy

   • Contact information
   • Physical presence
   • Policy pages
   • Third-party validation

4. Content Patterns

   • Balanced, nuanced presentation
   • Acknowledged limitations
   • Clear disclosures

Our Audit Plan:

• Review all author information for verifiability
• Audit citations for primary source linking
• Check business information consistency
• Review content for trust patterns (vs. manipulative)
• Ensure YMYL content has appropriate expert review

Key insight:

Trust isn’t about looking trustworthy. It’s about being verifiably trustworthy. AI can check.

Thanks everyone for the specific signals and patterns!