The general point is that you have to robustly compromise and satisfice all the goals. People tend to be rather good at it when taken as a group. (Any particula

The general point is that you have to robustly compromise and satisfice all the goals. People tend to be rather good at it when taken as a group. (Any particular person may be bad at a given subset of all problems.) It is a kind of optimality condition on all three goals. The robustness additionally means that should conditions change, the algorithm usually will become better not worse and should a degradation still happen, it will be graceful and not catastrophical. It's a hard and open problem in ML and especially ANN, design of robust solutions in the space. Most have really bad problems with it even when debiased.