Probability is the reflection of the state of your knowledge. If you know someone
might become the next Hitler, but you don't know who that might be, or if they're even on the tracks right now, then this is reflected by assigning the same probability of "hitlerism" to all the people on tracks.
And even if you had some suspicions, once you honestly factor in your certainty about predictive value of your suspicions, it multiplies down to nothing - so in practice, an uniform prior is a pretty good choice.
Yes, it's true that all this stuff is not computable in practice, that it's too complex - but it's not really an obstacle! Everything in life is like that. When you're cooking and a recipe calls for 200g of an ingredient, you're fine with anything between 180g or 220g, and don't care how much of it is water. A restaurant may need to do 200g ± 5g. A chemical plant may want to do 200g ± 0.5g, after first demoisturizing. Nobody tries to hit 200g to ± 1 dalton, because that would be ridiculously complex to do, and require to control for phenomena that most people don't even know exist.
We've been trading precision for compute time ever since humans first figured out that things can be measured. The solution is what it's always been: calculating using heuristics and approximations, while keeping tracks of the bounds of those heuristics and approximations.