ICLR 2021
Workshop paper

Engineering Fair Machine Learning Pipelines

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Data splits and data preparation during fairness mitigation are known to influence the performance of output models. We propose including protected attributes in stratification when splitting a dataset. We also describe fairness patterns for assembling fair pipelines that include data preparation, estimators, and mitigators. This paper introduces an open-source Python library lale.lib.aif360 that offers sklearn compatible implementations of fair stratification and fairness patterns.