I have the perspective of an informed layman as a programmer who hasn't messed with ML yet. Wouldn't the "safest" solution be a system with multiple algorithms and a consensus mechanism?
There is so much more than one monolithic NN since those are easy to saturate in terms of precision and recall with enough training data and features, but are not enough to provide good UX in any complex domain/ontology. So it makes more sense to have many different models trained on each subdomain/taxonomy so that each can be specialized and then combined orthogonally.
Then the question becomes "how do we orchestrate them?" Well, there is a lot of research from the 80s and 90s that kinda got left by the wayside due to hype cycles (see the last "AI winter"). My faves are Collagen and Ravenclaw. And there is a lot of literature around topic frame stack modelling, which can be combined with various expert systems or other logics. I am currently using CLIPS (PyKnow) with custom Ravenclaw implementation. I believe b4.ai is doing something similar without the logic/rules engine, and actually applying ML to topic selection as well. My systems are goal oriented so I like to give them a teleology for business reasons, which would not suffice for AGI ambitions.
TLDR data science isn't enough on its own. We need engineers to architect things properly to solve problems.
Typically in a regression setting with an ensemble learner you're using a kind of weighted mean, where the weights are selected based on cross-validation performance. This is sometimes called a "super learner". See van der Laan, Polley, and Hubbard 2007.
Note that this suffers from being similarly awful in terms of theory as a lot of other ML stuff. It is not, for example, the case that two apparently similar datasets or problem domains will produce similar super-learner weights. Which is disturbing, because it's easy to believe that say SVM does better at X and penalized ordered logit at Y, but it's hard to believe that they both do better seemingly at random.