I agree that plenty of people have been too cavalier about slapping together some models, predicting something, and calling it a day. On the other hand, it's not like fair sentencing or fair loan recommendation is a solved problem for humans either. There is evidence that, when carefully designed, algorithms can produce more equitable outcomes than humans, for example when deciding who and how to release on bail [1].

So I think we don't want to hand over sentencing decisions to a neural network that nobody understands, but I also think careful use of (simpler) machine learning can still improve a lot of our decision-making. The question is how much these decisions improve over what we have now. There's not exactly a surfeit of wise, highly trained ethicists who are happy to make consequential decisions all day. Many human decision-makers are quite flawed too, they just get to hide behind the opacity of being a human rather than an algorithm.

To that end, a whole "fair machine learning" field has sprung up over the past few years to study this. There are like a dozen papers in the area at NeurIPS and ICML every year. There's some progress.

[1] https://www.nber.org/papers/w23180