> I think the paper is sort of assuming something is happening in which the model forgets information about the scarce groups to make room for even more informa

> I think the paper is sort of assuming something is happening in which the model forgets information about the scarce groups to make room for even more information about the dense groups, but I don't think that really makes a lot of sense if you think it through Here's an example. A group of 10 people work together and try to decide where to go for lunch every day. 6 love pizza 3 love burgers 1 loves BBQ. Given the choice, the lover of each would pick their food. Imagine every day they need to make a decision, so they build an algorithmic "model" to best evaluate where to go for lunch. They do a vote. The majority 6 wins every day so they eat pizza every day all year. Imagine now instead they choose a different algorithmic model "everyone puts their one preference in a hat, someone picks one and we go there". Now over the course of the year we'd expect ~60% of the time pizza, 30% burgers and 10% BBQ. In both cases there was over/under representation of different groups, but the two different algorithms handled the "bias" differently. Now take that same type of concept and apply it to an ML model and that's similar to what the author is talking about.