I think you are overstating the purpose of machine learning quite a bit.
What machine learning does is function fitting, no more, no less. Whether this is causal, correlative, obvious, or obscure is irrelevant to the algorithm.
All it does is try to find parameters within a model function, which provide the best predictive power.
That's not entirely true. Causal inference is an enormous field and is just beginning to become more of a part of mainstream ML. ML is largely "minimize predictive error," but it's not limited to that.
There is an argument to be made though that we should be looking for causal connections rather than correlative when we think about what fairness looks like. Unfortunately, even with the many recent advances in causal network detection, we're not quite at a point where I would trust causal modeling for this.
Causal factors don't seem to be a road to avoid discrimination.
If we're looking at causal factors for classic examples of potentially discriminative classifiers e.g. loan default risk and crime reoffending risk, then no matter how you slice it the important causal factors for these things aren't only the objective measurements and things under your control but also different factors of influences, upbringing and cultural values. They're not the cause, and likely not the majority of the cause, but they're certainly a non-zero causal factor.
Having "bad" friends is not only a correlation, but a causal factor that affects these things - we're social animals, and our norms are affected by those around us. Would we consider fair to discriminate people in these ratings because of the friends they have? Do we want to ostracize e.g. ex-convicts by penalizing people who associate with them (so motivating them to choose not to associate), even if there's a true causal connection of that association increasing some risk?
Abuse of alcohol and certain drugs during pregnancy is not only a correlation, but a causal factor for these things (the mechanism IIRC was an decrease in risk avoidance and intelligence) - would we consider fair to discriminate people in these ratings because of what their mothers did?
Etc, etc - I have a bunch more in mind. And on top of that, many of these things will (in USA) be highly correlated with race for various historic and socioeconomic reasons, so taking that into account would still harm some races more than others. It seems that it just might be the interest of everyone just to avoid that huge can of worms.
the only way to look for causal connections is to brood force the probability space of correlations with the besy heuristics we can find. You present a false ditochomy, instead the parent used a syllogysm.
What machine learning does is function fitting, no more, no less. Whether this is causal, correlative, obvious, or obscure is irrelevant to the algorithm.
All it does is try to find parameters within a model function, which provide the best predictive power.