Maybe that example could be reduced to probability by saying you had an ensemble of different classifiers, each with its own hard cut. Then, you would ask, "what is the probability that a randomly selected judge would call Sally tall?"
This doesn't really reduce it though, it just changes the problem from degree-of-tallness to probability-of-opinion.
If I round up a million and people and survey them about whether -1, 0 and 1 are natural numbers, the probability of their opinions don't change whether -1, 0 or 1 are natural numbers.
The probabilities of judgement might be 0.1, 0.55 and 0.92, with confidence intervals. But each of these numbers has a set membership function and each of them is crisp. In fuzzy sets these belong to the set of natural numbers as 0.0, 0.0 and 1.0.
The useful concept that fuzzy sets brought to the table was being able to reason formally over uncertain values. Classical logic operates on crisp sets: all men are mortal, Socrates is a man, therefore Socrates is mortal. Fuzzy sets allows you to retain all the tools of logic over a wider range of statements.
This is widespread in control systems, where being able to reason formally about vague concepts like "too fast" is very helpful.
It's also common in caselaw, which is absolutely jampacked with fuzzy sets. "The purchaser at arm's length without notice". "The reasonable person, similarly circumstanced". "A duty of care". Different sets of facts in a case are ultimately resolved into binding rulings by a judge, but the argument itself has to follow logically from facts, legislation and precedent.
The law is also rightly aware of probabilities, especially in civil cases. "The balance of evidence" is sometimes called "the balance of probabilities". In criminal trials, "beyond reasonable doubt" is mostly about deciding on what follows from the probabilities of the facts presented.
