It takes knowledge to even know they're flawed, noisy, and disconnected. There's no reason to "correct" anything, unless you have knowledge that applying previously "understood" data has in fact produced deficient results in some application.
That's reinforcement learning -- an algorithm, which requires accurate knowledge acquisition, to be effective.
Every statistical machine learning algorithm, including RL, deals with noisy data. The process of fitting aims to remove the sampling noise, revealing the population distribution, thereby compressing it into a model.
The argument being advanced is that intelligence is the proposal of more parsimonious models, aka compression.
