Research

Individually Fair Gradient Boosting

ICLR

Authors

Published on

03/31/2021

Categories

ICLR Machine Learning

We consider the task of enforcing individual fairness in gradient boosting. Gradient boosting is a popular method for machine learning from tabular data, which arise often in applications where algorithmic fairness is a concern. At a high level, our approach is a functional gradient descent on a (distributionally) robust loss function that encodes our intuition of algorithmic fairness for the ML task at hand. Unlike prior approaches to individual fairness that only work with smooth ML models, our approach also works with non-smooth models such as decision trees. We show that our algorithm converges globally and generalizes. We also demonstrate the efficacy of our algorithm on three ML problems susceptible to algorithmic bias.

This paper has been published at ICLR 2021

Please cite our work using the BibTeX below.

@misc{vargo2021individually,
      title={Individually Fair Gradient Boosting}, 
      author={Alexander Vargo and Fan Zhang and Mikhail Yurochkin and Yuekai Sun},
      year={2021},
      eprint={2103.16785},
      archivePrefix={arXiv},
      primaryClass={cs.LG}
}
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