The values layer · Equality

Equal say on AI values

Expertise earns extra weight on questions that have a right answer. Values are not that kind of question. Confusing the two is how a small group ends up quietly deciding what everyone else's AI should care about.

On questions of value, everyone should weigh equally; on questions of fact, expertise should weigh more. That single distinction settles a lot. When an AI system encodes a judgment about what is fair, or what matters more, that is a values question, and there is no expert who is simply right about it the way a doctor is right about a diagnosis. The values layer for AI has to respect that difference, or it becomes a way for whoever builds the system to install their preferences as if they were findings.

Why values are not an expertise question

Because expertise is authority over what is true, and values are not claims about what is true. A brilliant engineer has no special standing to decide whose interests an algorithm should favor. They have standing to tell you how the system behaves, not what it should want. Treating value choices as technical choices is how those choices get made by the few and felt by the many.

Where expertise does belong

Everywhere there is a fact of the matter: how a model works, what its error rates are, what a change will do. That is the realm of the separate expertise question, and it is exactly why systems also need a way to route hard factual calls to qualified people. Facts and values both matter. They just answer to different masters.