I suspect few comments are missing the more subdle nature of the author's argument, which is buried in the rather complicated jargon of the field. The authors

I suspect few comments are missing the more subdle nature of the author's argument, which is buried in the rather complicated jargon of the field. The authors are NOT arguing that ML models cannot work in science, that the examples they mention for pseudoscience have fundamental methodological problems, nor even that classifying people based on ML is incorrect because correlation is not causation. Rather, they are warning the community about a problem with the epistemic value of results from applied ML, and argue that the problem is a cultural one. In other words, how do we _know_ that ML results are valid science? People working in ML itself are well aware of the garbage-in garbage-out problem of these models, but when these methods leave the field of machine learning and are applied in other sciences the experts in those fields do not know enough about these issues. So, because of the great success of ML models in the past, results from ML classifiers are taken as objective grounds to support dubious hypotheses. Part of their argument is that because extrapolation from data is seen as having little or even no bias, and because experts of most fields are not also ML experts, such publications with dubious hypotheses bypass the self-regulating mechanism of science, whereby bullshit research is called-out during review. And this problem, they also mention, is aggravated by the fact that data-driven methods can produce results faster than classical theory-based method hooking into bigger problem of academia that the number of publications is considered a proxy for success. So given the above I think it is easy to see that this could become dangerous since today science is considered a reliable tool to inform policymaking and political decisions at large. The authors use phrenology as an example to discuss the ethical implication of the issue because it is the most blatant example of socially corrosive pseudoresearch.