Thanks for the references. I will check them out once I get a chance. I do know one of these papers and from my understanding the modeling bias is on underrepresented features or the long tail, which again can be thought as a data problem that can be solved with better data collection.

I do agree that in the real world datasets are often biased because they represent the real world... and there are indeed modeling approaches to address such issues. (e.g., designing a loss function to up/down weight of certain types of examples). There's nothing new about this, it's been known in ML for decades.