Yes, this is a problem - the most challenging samples might not even be present in your training data. This means your model will not perform well if real world data has lots of challenging samples.
This can be partially solved if we make some assumptions about your labeller:
1. they have still picked enough challenging samples.
2. their preferences are still based on features you care about.
3. he labelled the challenging samples correctly.
And probably some other assumptions should hold for distribution of labels, etc. But what we can do in this situation is first try to model that labeller preferences, by training a binary classifier - how likely he would choose this sample for labelling from the real-world distribution? If we train that classifier, we can then assign its confidence as a sample weight when preparing our training dataset (less likely samples get more weight). This would force our main classifier to pay more attention to the challenging samples during training.
This could help somewhat if all assumptions hold, but in practice I would not expect much improvement, and the solution above can easily make it worse - this problem needs to be solved by better labelling.
How did you solve it?
