A Suite of Fairness Datasets for Tabular Classification
This provides a standardized resource for researchers in fairness-aware machine learning, though it is incremental as it focuses on data collection rather than new algorithms.
The authors addressed the lack of diverse datasets in fairness research by introducing a suite of 20 fairness datasets with metadata for tabular classification, aiming to enable more rigorous experimental evaluations in future studies.
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.