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.

Yes, the problem in all these discussions is that unless you engage with them on broader epistemological grounds, you’re just occluding the a priori biases.

Engineering, as a discipline, has an unfortunate history of not wanting to engage with the social and political conditions under which its work occurs, but that becomes entirely untenable for how a lot of ML is put to use (if you’re trying to be honest anyway).

>Engineering, as a discipline, has an unfortunate history of not wanting to engage with the social and political conditions under which its work occur

Are there any industries that actually do this well?