Why AI Governance Needs a Decentralized Quality Assurance Layer
Our background is in quality assurance and software engineering, so whenever we see a new technology becoming important, one question naturally comes up: How do we know it actually works?
That question becomes more complicated with AI. We already have benchmarks, model evaluations, safety tests, red-team exercises, and
leaderboards. But there is still another question underneath all of them: Who should we trust to evaluate the evaluator?
If an organization builds an AI system, tests that system, publishes the results, and tells us the model passed, we are still placing a lot of trust in one organization.
In traditional software quality assurance, that would make us uncomfortable. We separate responsibilities for a reason. We run independent tests. We preserve evidence. We investigate conflicting results. And when something passes a quality gate, ideally we can explain why it passed.
That led us to an idea: what if AI quality could be...
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