# Achieving Equalized Odds by Resampling Sensitive Attributes

arXiv: https://arxiv.org/abs/2006.04292 · HTML: https://mlsanity.com/paper/2006.04292/

## Key facts

- Cited by 42 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 Jun 2020.

## Built on by (most cited first, 30 of 42)

- [Bias Mitigation for Machine Learning Classifiers: A Comprehensive Survey](https://mlsanity.com/paper/2207.07068/) (2022)
- [Improving Conditional Coverage via Orthogonal Quantile Regression](https://mlsanity.com/paper/2106.00394/) (2021)
- [Mitigating the Risk of Health Inequity Exacerbated by Large Language Models](https://mlsanity.com/paper/2410.05180/) (2024)
- [What-is and How-to for Fairness in Machine Learning: A Survey, Reflection, and Perspective](https://mlsanity.com/paper/2206.04101/) (2022)
- [Data Representativity for Machine Learning and AI Systems](https://mlsanity.com/paper/2203.04706/) (2022)
- [Blackbox Post-Processing for Multiclass Fairness](https://mlsanity.com/paper/2201.04461/) (2022)
- [Bounding and Approximating Intersectional Fairness through Marginal Fairness](https://mlsanity.com/paper/2206.05828/) (2022)
- [Attainability and Optimality: The Equalized Odds Fairness Revisited](https://mlsanity.com/paper/2202.11853/) (2022)
- [Learning Optimal Fair Scoring Systems for Multi-Class Classification](https://mlsanity.com/paper/2304.05023/) (2023)
- [Distributionally Generative Augmentation for Fair Facial Attribute Classification](https://mlsanity.com/paper/2403.06606/) (2024)
- [EqGNN: Equalized Node Opportunity in Graphs](https://mlsanity.com/paper/2108.08800/) (2021)
- [Toward Operationalizing Pipeline-aware ML Fairness: A Research Agenda for Developing Practical Guidelines and Tools](https://mlsanity.com/paper/2309.17337/) (2023)
- [Distribution-Free Statistical Dispersion Control for Societal Applications](https://mlsanity.com/paper/2309.13786/) (2023)
- [FairBalance: How to Achieve Equalized Odds With Data Pre-processing](https://mlsanity.com/paper/2107.08310/) (2021)
- [Estimating and Controlling for Equalized Odds via Sensitive Attribute Predictors](https://mlsanity.com/paper/2207.12497/) (2022)
- [CFARnet: deep learning for target detection with constant false alarm rate](https://mlsanity.com/paper/2208.02474/) (2022)
- [Equalised Odds is not Equal Individual Odds: Post-processing for Group and Individual Fairness](https://mlsanity.com/paper/2304.09779/) (2023)
- [FairICP: Encouraging Equalized Odds via Inverse Conditional Permutation](https://mlsanity.com/paper/2404.05678/) (2024)
- [Mitigating Confounding in Speech-Based Dementia Detection through Weight Masking](https://mlsanity.com/paper/2506.05610/) (2025)
- [Hard Choices and Hard Limits for Artificial Intelligence](https://mlsanity.com/paper/2105.07852/) (2021)
- [Learning to Increase the Power of Conditional Randomization Tests](https://mlsanity.com/paper/2207.01022/) (2022)
- [Mitigating Algorithmic Bias with Limited Annotations](https://mlsanity.com/paper/2207.10018/) (2022)
- [Fair Supervised Learning with A Simple Random Sampler of Sensitive Attributes](https://mlsanity.com/paper/2311.05866/) (2023)
- [Post-processing for Fair Regression via Explainable SVD](https://mlsanity.com/paper/2504.03093/) (2025)
- [Learning to Detect with Constant False Alarm Rate](https://mlsanity.com/paper/2206.05747/) (2022)
- [I Prefer not to Say: Protecting User Consent in Models with Optional Personal Data](https://mlsanity.com/paper/2210.13954/) (2022)
- [Uncertainty in Fairness Assessment: Maintaining Stable Conclusions Despite Fluctuations](https://mlsanity.com/paper/2302.01079/) (2023)
- [A Counterfactual Fair Model for Longitudinal Electronic Health Records via Deconfounder](https://mlsanity.com/paper/2308.11819/) (2023)
- [Will the Inclusion of Generated Data Amplify Bias Across Generations in Future Image Classification Models?](https://mlsanity.com/paper/2410.10160/) (2024)
- [Auditing and Enforcing Conditional Fairness via Optimal Transport](https://mlsanity.com/paper/2410.14029/) (2024)

## Abstract (arXiv)

We present a flexible framework for learning predictive models that approximately satisfy the equalized odds notion of fairness. This is achieved by introducing a general discrepancy functional that rigorously quantifies violations of this criterion. This differentiable functional is used as a penalty driving the model parameters towards equalized odds. To rigorously evaluate fitted models, we develop a formal hypothesis test to detect whether a prediction rule violates this property, the first such test in the literature. Both the model fitting and hypothesis testing leverage a resampled version of the sensitive attribute obeying equalized odds, by construction. We demonstrate the applicability and validity of the proposed framework both in regression and multi-class classification problems, reporting improved performance over state-of-the-art methods. Lastly, we show how to incorporate techniques for equitable uncertainty quantification---unbiased for each group under study---to communicate the results of the data analysis in exact terms.