Why AI Evaluation Needs Independence, Not Just Human Judgment

Ambarish Majumdar is a Marketing Science Partner at Meta, where he uses his SME knowledge in Marketing Science to better AI models.

On September 16, 2026, Meta CEO Mark Zuckerberg rejected calls for a coordinated slowdown in artificial intelligence (AI) development. Citing competition, liability and each lab’s responsibility to train safely, he made one point that deserves more attention: Independent evaluators are an “industry best practice.”

That isn’t a minor procedural detail. It’s the governance mechanism that can make a market-led approach to AI safety credible. If trust and alignment are competitive capabilities, and companies face meaningful consequences when systems cause harm, leaders need evidence that hasn’t been shaped by the same pressures as the launch decision.

I previously wrote about why great AI systems need human judgment and subject matter expertise. Independence addresses a different problem. Human reviewers can apply thoughtful standards and still be grading their own exam. The question is whether those producing the evidence are free to challenge the builders—and whether their findings can survive an inconvenient result.

Independence Breaks The Builder’s Conflict Of Interest

Product teams know their systems best, and their participation is essential. But they also carry delivery commitments, sunk costs and understandable confidence in their choices. This doesn’t imply bad faith. It’s why mature disciplines separate making a claim from independently testing it.

Familiarity also creates blind spots. Builders know the intended workflow, so they may test the system as it was meant to be used. They know the mitigations, so they may give those controls more credit than an unfamiliar user would. Independence adds a party whose success isn’t defined by shipping on time.