The thing is the ML models don't try to be interpretable. Instead people try to find models that make correct predictions. AFAICT, that is also the appropriate

The thing is the ML models don't try to be interpretable. Instead people try to find models that make correct predictions. AFAICT, that is also the appropriate role for simpler methods like linear regression, unless you have reason to believe your model is robust to any missing "unknown" features and any included "spurious" ones. In that case you are probably dealing with a model based on domain knowledge and not even running a standard linear regression. People (over-)interpret the coefficients all the time though, I actually think this is a huge problem.