# Auditing and Achieving Intersectional Fairness in Classification Problems

arXiv: https://arxiv.org/abs/1911.01468 · HTML: https://mlsanity.com/paper/1911.01468/

## Key facts

- Cited by 27 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 4 Nov 2019.

## Built on by (most cited first, 27 of 27)

- [Bias Mitigation for Machine Learning Classifiers: A Comprehensive Survey](https://mlsanity.com/paper/2207.07068/) (2022)
- [Towards Intersectionality in Machine Learning: Including More Identities, Handling Underrepresentation, and Performing Evaluation](https://mlsanity.com/paper/2205.04610/) (2022)
- [A Survey on Intersectional Fairness in Machine Learning: Notions, Mitigation, and Challenges](https://mlsanity.com/paper/2305.06969/) (2023)
- [Fairness in Recommender Systems: Research Landscape and Future Directions](https://mlsanity.com/paper/2205.11127/) (2022)
- [Statistical Inference for Fairness Auditing](https://mlsanity.com/paper/2305.03712/) (2023)
- [InfoFair: Information-Theoretic Intersectional Fairness](https://mlsanity.com/paper/2105.11069/) (2021)
- [Auditing of AI: Legal, Ethical and Technical Approaches](https://mlsanity.com/paper/2407.06235/) (2024)
- [Cross-Institutional Transfer Learning for Educational Models: Implications for Model Performance, Fairness, and Equity](https://mlsanity.com/paper/2305.00927/) (2023)
- [Identifying Biased Subgroups in Ranking and Classification](https://mlsanity.com/paper/2108.07450/) (2021)
- [The Intersectionality Problem for Algorithmic Fairness](https://mlsanity.com/paper/2411.02569/) (2024)
- [Overcoming Fairness Trade-offs via Pre-processing: A Causal Perspective](https://mlsanity.com/paper/2501.14710/) (2025)
- [Multi-fairness under class-imbalance](https://mlsanity.com/paper/2104.13312/) (2021)
- [Deep Metric Learning for the Hemodynamics Inference with Electrocardiogram Signals](https://mlsanity.com/paper/2308.04650/) (2023)
- [Fairness-Aware Naive Bayes Classifier for Data with Multiple Sensitive Features](https://mlsanity.com/paper/2202.11499/) (2022)
- [Synthetic Data Generation for Intersectional Fairness by Leveraging Hierarchical Group Structure](https://mlsanity.com/paper/2405.14521/) (2024)
- [Bias Mitigation Framework for Intersectional Subgroups in Neural Networks](https://mlsanity.com/paper/2212.13014/) (2022)
- [Fair Without Leveling Down: A New Intersectional Fairness Definition](https://mlsanity.com/paper/2305.12495/) (2023)
- [Intersectional Divergence: Measuring Fairness in Regression](https://mlsanity.com/paper/2505.00830/) (2025)
- [Size-adaptive Hypothesis Testing for Fairness](https://mlsanity.com/paper/2506.10586/) (2025)
- [Fairness of Explanations in Artificial Intelligence (AI): A Unifying Framework, Axioms, and Future Direction toward Responsible AI](https://mlsanity.com/paper/2605.09852/) (2026)
- [Using Open-Ended Stressor Responses to Predict Depressive Symptoms across Demographics](https://mlsanity.com/paper/2211.07932/) (2022)
- [An Efficient Framework for Monitoring Subgroup Performance of Machine Learning Systems](https://mlsanity.com/paper/2212.08312/) (2022)
- [Intersectional Fairness: A Fractal Approach](https://mlsanity.com/paper/2302.12683/) (2023)
- [Detecting Interpretable Subgroup Drifts](https://mlsanity.com/paper/2408.14682/) (2024)
- [Transparent and Fair Profiling in Employment Services: Evidence from Switzerland](https://mlsanity.com/paper/2509.11847/) (2025)
- [Cost Efficient Fairness Audit Under Partial Feedback](https://mlsanity.com/paper/2510.03734/) (2025)
- [Multi-Sensitive Distributional Fairness: Joint Wasserstein Audits and Shapley Allocation](https://mlsanity.com/paper/2610.19252/) (2026)

## Abstract (arXiv)

Machine learning algorithms are extensively used to make increasingly more consequential decisions about people, so achieving optimal predictive performance can no longer be the only focus. A particularly important consideration is fairness with respect to race, gender, or any other sensitive attribute. This paper studies intersectional fairness, where intersections of multiple sensitive attributes are considered. Prior research has mainly focused on fairness with respect to a single sensitive attribute, with intersectional fairness being comparatively less studied despite its critical importance for the safety of modern machine learning systems. We present a comprehensive framework for auditing and achieving intersectional fairness in classification problems: we define a suite of metrics to assess intersectional fairness in the data or model outputs by extending known single-attribute fairness metrics, and propose methods for robustly estimating them even when some intersectional subgroups are underrepresented. Furthermore, we develop post-processing techniques to mitigate any detected intersectional bias in a classification model. Our techniques do not rely on any assumptions regarding the underlying model and preserve predictive performance at a guaranteed level of fairness. Finally, we give guidance on a practical implementation, showing how the proposed methods perform on a real-world dataset.