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What is E-E-A-T (in the AI era)?

E-E-A-T (Experience, Expertise, Authoritativeness, Trustworthiness) is the set of signals Google uses to evaluate content credibility — in the AI era, visible human authorship, verifiable sources, and AI-use disclosure have come to count more.

In short

  • Four signals: experience, expertise, authoritativeness, and trustworthiness.
  • For AI-generated content, the Trustworthiness signal has gained weight.
  • Honest AI-use disclosure + visible human authorship help.

Full definition

E-E-A-T is the evolution of the older E-A-T (Google added 'Experience' in 2022). For AI-assisted journalism, best practices involve keeping a visible human byline, citing sources, and publishing a public editorial AI policy.

Google doesn't have a perfect automated tool to detect AI — but it evaluates E-E-A-T via proxies: author bio, domain authority, cited sources, correction frequency, public editorial policy. In 2024-2026, Google reinforced helpful-content guidance, focusing on the outcome for the reader more than on the tool used.

For a newsroom adopting AI, the practical path is: keep a human byline on every story (even when AI produced the first draft), cite verifiable sources in the text, publish a public editorial AI policy, and disclose AI use when relevant. That package sustains E-E-A-T even at high scale.

How it works

  1. Experience: signals that the author has direct lived experience with the subject (video, photo, first-person testimony). Harder with AI, but attributing the story to a human journalist who validates the content helps.
  2. Expertise: author credentials and history in the niche. Complete bios, links to prior work, presence on professional networks.
  3. Authoritativeness: domain authority (backlinks, age, citation by other established publications).
  4. Trustworthiness: clear editorial policy, AI disclosure, cited sources, visible correction when there's an error.

Practical example

A sports publication publishes a story with an AI first draft + human journalist review. The journalist's byline appears (with bio and link to their other stories), a footer note indicates AI use in the first draft, sources are cited in the text, and there's a link to the editorial AI policy. This combination scores well on E-E-A-T even in a newsroom producing volume with AI.

E-E-A-T (in the AI era) vs E-A-T (the version before 2022)

E-A-T had three signals: Expertise, Authoritativeness, Trustworthiness. Google added Experience in 2022 to capture the difference between content from someone who lived the topic and derivative content. The addition reinforces that field journalism (or at least human validation of AI content) carries higher weight.

Frequently asked questions

Does AI-generated content hurt E-E-A-T?

Not automatically. Google made it clear that it evaluates the outcome for the reader, not the tool. AI content + human review + sources + disclosure scores well. Pure AI content without review or disclosure tends to score poorly.

How do you signal Experience in AI content?

It's hard to signal Experience in 100% AI content — Experience requires direct lived experience. The practical shortcut is a human byline from the journalist who validated the content (even if AI produced the first draft) and a link to the author's prior work demonstrating experience in the niche.

See how Typedit uses e-e-a-t (in the ai era)

The verifiable editorial AI platform applies this concept in production — at Brazilian newsrooms with 10M+ monthly readers.

Related terms

What is E-E-A-T (in the AI era)? — Typedit glossary | Typedit