# Fairness with Overlapping Groups

arXiv: https://arxiv.org/abs/2006.13485 · HTML: https://mlsanity.com/paper/2006.13485/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 Jun 2020.

## Built on by (most cited first, 12 of 12)

- [Towards Intersectionality in Machine Learning: Including More Identities, Handling Underrepresentation, and Performing Evaluation](https://mlsanity.com/paper/2205.04610/) (2022)
- [CLIP the Bias: How Useful is Balancing Data in Multimodal Learning?](https://mlsanity.com/paper/2403.04547/) (2024)
- [Fairness for Image Generation with Uncertain Sensitive Attributes](https://mlsanity.com/paper/2106.12182/) (2021)
- [A Reduction to Binary Approach for Debiasing Multiclass Datasets](https://mlsanity.com/paper/2205.15860/) (2022)
- [Learning Optimal Fair Scoring Systems for Multi-Class Classification](https://mlsanity.com/paper/2304.05023/) (2023)
- [Optimizing Black-box Metrics with Iterative Example Weighting](https://mlsanity.com/paper/2102.09492/) (2021)
- [Intersectional Unfairness Discovery](https://mlsanity.com/paper/2405.20790/) (2024)
- [fairlib: A Unified Framework for Assessing and Improving Classification Fairness](https://mlsanity.com/paper/2205.01876/) (2022)
- [Human-in-the-loop Fairness: Integrating Stakeholder Feedback to Incorporate Fairness Perspectives in Responsible AI](https://mlsanity.com/paper/2312.08064/) (2023)
- [Beyond Impossibility: Balancing Sufficiency, Separation and Accuracy](https://mlsanity.com/paper/2205.12327/) (2022)
- [Bias Mitigation Framework for Intersectional Subgroups in Neural Networks](https://mlsanity.com/paper/2212.13014/) (2022)
- [Measuring and Mitigating Bias for Tabular Datasets with Multiple Protected Attributes](https://mlsanity.com/paper/2405.19300/) (2024)

## Abstract (arXiv)

In algorithmically fair prediction problems, a standard goal is to ensure the equality of fairness metrics across multiple overlapping groups simultaneously. We reconsider this standard fair classification problem using a probabilistic population analysis, which, in turn, reveals the Bayes-optimal classifier. Our approach unifies a variety of existing group-fair classification methods and enables extensions to a wide range of non-decomposable multiclass performance metrics and fairness measures. The Bayes-optimal classifier further inspires consistent procedures for algorithmically fair classification with overlapping groups. On a variety of real datasets, the proposed approach outperforms baselines in terms of its fairness-performance tradeoff.