Generalized Disparate Impact for Configurable Fairness Solutions in ML

Luca Giuliani, Eleonora Misino, Michele Lombardi
5/29/2023
cs.LG

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Abstract

We make two contributions in the field of AI fairness over continuous protected attributes. First, we show that the Hirschfeld-Gebelein-Renyi (HGR) indicator (the only one currently available for such a case) is valuable but subject to a few crucial limitations regarding semantics, interpretability, and robustness. Second, we introduce a family of indicators that are: 1) complementary to HGR in terms of semantics; 2) fully interpretable and transparent; 3) robust over finite samples; 4) configurable to suit specific applications. Our approach also allows us to define fine-grained constraints to permit certain types of dependence and forbid others selectively. By expanding the available options for continuous protected attributes, our approach represents a significant contribution to the area of fair artificial intelligence.

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Cite this paper

@article{giuliani2023generalized,
  title  = {Generalized Disparate Impact for Configurable Fairness Solutions in ML},
  author = {Luca Giuliani and Eleonora Misino and Michele Lombardi},
  year   = {2023},
  eprint = {2305.18504},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2305.18504v1},
  journal = {ICML 2023 2023}
}

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