A Suite of Fairness Datasets for Tabular Classification
Martin Hirzel Michael Feffer
There have been many papers with algorithms for improving fairness of
machine-learning classifiers for tabular data. Unfortunately, most use only
very few datasets for their experimental evaluation. We introduce a suite of
functions for fetching 20 fairness datasets and providing associated fairness
metadata. Hopefully, these will lead to more rigorous experimental evaluations
in future fairness-aware machine learning research.