The problem is the data is biased. The ML algorithm doesn't know whether the bias is a natural part of the data or artificially induced. Garbage In - Garbage Out.
You're making up excuses. The data is not 'biased', it just is, nor is it garbage - it's not made up, no one is lying or falsifying data or anything like that. If your theory cannot handle clean data from a real-world problem, that's a big problem (especially if there are more sophisticated alternatives which can handle it).
Biased data is a real thing and this is a great example. No method can solve the problem you've given without additional information.
Yann LeCun, now of Facebook, was interviewed by The Register. It is interesting that his view of AI is apparently that of a prediction tool:
"In some ways you could say intelligence is all about prediction," he explained. "What you can identify in intelligence is it can predict what is going to happen in the world with more accuracy and more time horizon than others."
rather than of a world optimizer. This is not very surprising, given his background in handwriting and image recognition. This "AI as intelligence augmentation" view appears to be prevalent among the AI researchers in general.