most citedTowards Intersectionality in Machine Learning: Including More Identities, Handling Underrepresentation, and Performing Evaluation

90 citations · 90 across the 1 of their papers we have counts for

collaborators

1 paper

cs.LG2022★ 90 cited

Towards Intersectionality in Machine Learning: Including More Identities, Handling Underrepresentation, and Performing Evaluation

Angelina Wang, Vikram V. Ramaswamy, Olga Russakovsky

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 gr…