Towards Intersectionality in Machine Learning: Including More Identities, Handling Underrepresentation, and Performing Evaluation. (arXiv:2205.04610v1 [cs.LG])

Research in machine learning fairness has historically considered a single
binary demographic attribute; however, the reality is of course far more
complicated. In this work, we grapple with questions that arise along three
stages of the machine learning pipeline when incorporating intersectionality as
multiple demographic attributes: (1) which demographic attributes to include as
dataset labels, (2) how to handle the progressively smaller size of subgroups
during model training, and (3) how to move beyond existing evaluation metrics
when benchmarking model …

arxiv evaluation learning machine machine learning underrepresentation