A Suite of Fairness Datasets for Tabular Classification

Martin Hirzel, Michael Feffer
7/31/2023
cs.LGcs.AIcs.CY

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Abstract

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.

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Cite this paper

@article{hirzel2023suite,
  title  = {A Suite of Fairness Datasets for Tabular Classification},
  author = {Martin Hirzel and Michael Feffer},
  year   = {2023},
  eprint = {2308.00133},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2308.00133v1}
}

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