mlsanityData to 14 Oct 2026

Generalized Disparate Impact for Configurable Fairness Solutions in ML

arXiv 2305.18504 ↗First posted 29 May 2023cs.LGcited by 7

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Abstract (arXiv)

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.

Key facts. Cited by 7 papers in the map (data through 14 Oct 2026). Builds on 9 of its 21 references that are in the map. First posted on arXiv 29 May 2023.

Builds on

9 of 21 references.

References · 9

  1. 2016Equality of Opportunity in Supervised Learningcited by 2,007
  2. 2019A Survey on Bias and Fairness in Machine Learningcited by 1,917
  3. 2014Certifying and removing disparate impactcited by 715
  4. 2016Fairness Beyond Disparate Treatment & Disparate Impact: Learning Classification without Disparate Mistreatmentcited by 369
  5. 2018Empirical Risk Minimization under Fairness Constraintscited by 226
  6. 2019Learning Optimal and Fair Decision Trees for Non-Discriminative Decision-Makingcited by 75
  7. 2020Lagrangian Duality for Constrained Deep Learningcited by 74
  8. 2018FNNC: Achieving Fairness through Neural Networkscited by 43
  9. 2020Teaching the Old Dog New Tricks: Supervised Learning with Constraintscited by 16

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Cited by year

Citing papers per year: 2024: 1; 2025: 6.

7 papers.

  1. 2025Fair Representation Learning for Continuous Sensitive Attributes using Expectation of Integral Probability Metricscited by 7
  2. 2025Fairness-Aware Grouping for Continuous Sensitive Variables: Application for Debiasing Face Analysis with respect to Skin Tonecited by 4
  3. 2025Evaluating Fairness and Mitigating Bias in Machine Learning: A Novel Technique using Tensor Data and Bayesian Regressioncited by 1
  4. 2024Using AI Alignment Theory to understand the potential pitfalls of regulatory frameworkscited by 0
  5. 2025Robust Non-Linear Correlations via Polynomial Regressioncited by 0
  6. 2025Extending Fair Null-Space Projections for Continuous Attributes to Kernel Methodscited by 0
  7. 2025Incorporating Fairness in Neighborhood Graphs for Fair Spectral Clusteringcited by 0