Right, probably it’s better to look at the model and call it “fair model” as long as the model output error on a given class of inputs is inversely proportional

Right, probably it’s better to look at the model and call it “fair model” as long as the model output error on a given class of inputs is inversely proportional to the percent of this class in training data (eg more training data of the same class means less error and less training data means more error). I can’t recall a single ML model which was not fair though I mostly deal with texts, not images.