# Noise-tolerant fair classification

arXiv: https://arxiv.org/abs/1901.10837 · HTML: https://mlsanity.com/paper/1901.10837/

## Key facts

- Cited by 40 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 Jan 2019.

## Built on by (most cited first, 30 of 40)

- [Bias Mitigation for Machine Learning Classifiers: A Comprehensive Survey](https://mlsanity.com/paper/2207.07068/) (2022)
- [Data Collection and Quality Challenges in Deep Learning: A Data-Centric AI Perspective](https://mlsanity.com/paper/2112.06409/) (2021)
- [Sample Selection for Fair and Robust Training](https://mlsanity.com/paper/2110.14222/) (2021)
- [Learning Fair Classifiers with Partially Annotated Group Labels](https://mlsanity.com/paper/2111.14581/) (2021)
- [Evaluating Fairness of Machine Learning Models Under Uncertain and Incomplete Information](https://mlsanity.com/paper/2102.08410/) (2021)
- [Fairness for Image Generation with Uncertain Sensitive Attributes](https://mlsanity.com/paper/2106.12182/) (2021)
- [Beyond Images: Label Noise Transition Matrix Estimation for Tasks with Lower-Quality Features](https://mlsanity.com/paper/2202.01273/) (2022)
- [Privacy and Fairness in Federated Learning: on the Perspective of Trade-off](https://mlsanity.com/paper/2306.14123/) (2023)
- [A Sociotechnical View of Algorithmic Fairness](https://mlsanity.com/paper/2110.09253/) (2021)
- [Fair Classification with Adversarial Perturbations](https://mlsanity.com/paper/2106.05964/) (2021)
- [Fairness-Aware PAC Learning from Corrupted Data](https://mlsanity.com/paper/2102.06004/) (2021)
- [Weak Proxies are Sufficient and Preferable for Fairness with Missing Sensitive Attributes](https://mlsanity.com/paper/2210.03175/) (2022)
- [Fair Ranking with Noisy Protected Attributes](https://mlsanity.com/paper/2211.17067/) (2022)
- [Can Less be More? When Increasing-to-Balancing Label Noise Rates Considered Beneficial](https://mlsanity.com/paper/2107.05913/) (2021)
- [Measuring Model Fairness under Noisy Covariates: A Theoretical Perspective](https://mlsanity.com/paper/2105.09985/) (2021)
- [Toward Operationalizing Pipeline-aware ML Fairness: A Research Agenda for Developing Practical Guidelines and Tools](https://mlsanity.com/paper/2309.17337/) (2023)
- [When Fairness Meets Privacy: Fair Classification with Semi-Private Sensitive Attributes](https://mlsanity.com/paper/2207.08336/) (2022)
- [Combating Health Misinformation in Social Media: Characterization, Detection, Intervention, and Open Issues](https://mlsanity.com/paper/2211.05289/) (2022)
- [When Fair Classification Meets Noisy Protected Attributes](https://mlsanity.com/paper/2307.03306/) (2023)
- [Prisoners of Their Own Devices: How Models Induce Data Bias in Performative Prediction](https://mlsanity.com/paper/2206.13183/) (2022)
- [How Far Can Fairness Constraints Help Recover From Biased Data?](https://mlsanity.com/paper/2312.10396/) (2023)
- [Breaking Fair Binary Classification with Optimal Flipping Attacks](https://mlsanity.com/paper/2204.05472/) (2022)
- [Toward the Tradeoffs between Privacy, Fairness and Utility in Federated Learning](https://mlsanity.com/paper/2311.18190/) (2023)
- [Outlier-Robust Group Inference via Gradient Space Clustering](https://mlsanity.com/paper/2210.06759/) (2022)
- [Hyper-parameter Tuning for Fair Classification without Sensitive Attribute Access](https://mlsanity.com/paper/2302.01385/) (2023)
- [A Survey on Preserving Fairness Guarantees in Changing Environments](https://mlsanity.com/paper/2211.07530/) (2022)
- [Group Fairness with Uncertainty in Sensitive Attributes](https://mlsanity.com/paper/2302.08077/) (2023)
- [Sampling Individually-Fair Rankings that are Always Group Fair](https://mlsanity.com/paper/2306.11964/) (2023)
- [Trading-off price for data quality to achieve fair online allocation](https://mlsanity.com/paper/2306.13440/) (2023)
- [Towards Fair Classification against Poisoning Attacks](https://mlsanity.com/paper/2210.09503/) (2022)

## Abstract (arXiv)

Fairness-aware learning involves designing algorithms that do not discriminate with respect to some sensitive feature (e.g., race or gender). Existing work on the problem operates under the assumption that the sensitive feature available in one's training sample is perfectly reliable. This assumption may be violated in many real-world cases: for example, respondents to a survey may choose to conceal or obfuscate their group identity out of fear of potential discrimination. This poses the question of whether one can still learn fair classifiers given noisy sensitive features. In this paper, we answer the question in the affirmative: we show that if one measures fairness using the mean-difference score, and sensitive features are subject to noise from the mutually contaminated learning model, then owing to a simple identity we only need to change the desired fairness-tolerance. The requisite tolerance can be estimated by leveraging existing noise-rate estimators from the label noise literature. We finally show that our procedure is empirically effective on two case-studies involving sensitive feature censoring.