[mjburgess]

In general, you can't, and most of reality isnt knowable. That's a problem with reality, and us.

I'd take a bayesian approach across an ensemble of models based on the risk of each being right/wrong.

Consider whether Drug A causes or cures cancer. If there's some circumstantial evidence of it causing cancer at rate X in population Y with risk factors Z -- and otherwise broad circumstial evidence of it curing at rate A in pop B with features C...

then what? Then create various scenarios under these (likely contradictory) assumptions. Formulate an appropriate risk. Derive some implied policies.

This is the reality of how almost all actual decisions are made in life, and necessarily so.

The real danger is when ML is used to replace that, and you end up with extremely fragile systems that automate actions of unknown risk -- on the basis they were "99.99%", "accurate", ie., considered uncontrolled experimental condition E1 and not E2...10_0000 which actually occur