ML objectives need to change (Score 1)

Right now, ML seems to be fixated on black box algorithms that produce an output conclusion that should be "good enough" for most users while obfuscating any of the details (or simply using methods that aren't interpretable). It is very clear to me that we need to move towards a 100% interpretability standard. I participated in one of these covid treatment prediction projects and I'll say the focus from the medical research staff was on "a simple decision metric to add into already existing metrics to help make triage more reliable" What these doctors really needed was something that would highlight the most important variables (FiO2, Age, and temperature) in cases where they were relevant. A black box "yes/no" prediction was just not that useful. We found that none of the easily interpretable techniques (LASSO, Decision Trees, PCA, etc...) we worked with did a particularly good job -- we achieved our best fit with XGBoost (which is not exactly easy for a human to gain enlightenment from) and had to do extensive analysis to determine the most important variables in each decision.