ML models don't exist in a vacuum, but they do exist in an empirical reality. And in an empirical reality, there is always the fundamental unbiased measure of

ML models don't exist in a vacuum, but they do exist in an empirical reality. And in an empirical reality, there is always the fundamental unbiased measure of success: predicting whatever it is the model is built to predict. And this, I think is the knife that separates the different schools of thought on the issue. People who are judging whether an ML model is "good" or "bad" based on this criteria necessarily see the accusation of "bias" as a claim that their model is not successfully predicting things. They rightfully retort that they would do a better job with an unbiased dataset. To argue they are their models are always wrong on their terms is to argue that there is a Ken Thompson-like hack in their mathematics. [1] On the other hand, people who judge ML models by criteria like how they might be used or interpreted by laypeople are fundamentally talking about something other than ML models-qua-mathematical models. To the modelers, you might as well be arguing that the theory of nuclear fission is biased against the Japanese. But you are not actually talking about the empirical quality of their model, and so on your own terms you are correct. The models can be used improperly, and researchers should be careful about how their findings are perceived. [1] https://wiki.c2.com/?TheKenThompsonHack https://wiki.c2.com/?TheKenThompsonHack