I'm put off by using a complex model as a definition. I've always seen a model as an imperfect approximation, where there's always room for improvement. A good model of humans should be able to look at a candidate and decide whether it's human with some probability p of a false positive and q of a false negative. A model that uses statistical data can potentially improve by gathering more information.

A definition, on the other hand, is deterministic. Taking a model as a definition is basically declaring that your model is correct and cuts off any avenue for improvement. Definitions are usually used for simpler concepts that can be readily articulated. It's possible to brute-force a definition by making a list of all objects in the universe that satisfy it. So, I could conceivably make a list of shirts and then define "shirt" to mean any object in the list. However, I don't think that's quite what you had in mind.