# Towards Fair Classifiers Without Sensitive Attributes: Exploring Biases in Related Features

arXiv: https://arxiv.org/abs/2104.14537 · HTML: https://mlsanity.com/paper/2104.14537/

## Key facts

- Cited by 45 papers in the map (data through 14 Oct 2026).
- Builds on 19 of its 43 references that are in the map.
- First posted on arXiv 29 Apr 2021.

## Builds on (most cited first, 19 of 19)

- [Equality of Opportunity in Supervised Learning](https://mlsanity.com/paper/1610.02413/) (2016)
- [A Survey on Bias and Fairness in Machine Learning](https://mlsanity.com/paper/1908.09635/) (2019)
- [Certifying and removing disparate impact](https://mlsanity.com/paper/1412.3756/) (2014)
- [Mitigating Unwanted Biases with Adversarial Learning](https://mlsanity.com/paper/1801.07593/) (2018)
- [On Fairness and Calibration](https://mlsanity.com/paper/1709.02012/) (2017)
- [Fairness Without Demographics in Repeated Loss Minimization](https://mlsanity.com/paper/1806.08010/) (2018)
- [Fairness without Demographics through Adversarially Reweighted Learning](https://mlsanity.com/paper/2006.13114/) (2020)
- [The Variational Fair Autoencoder](https://mlsanity.com/paper/1511.00830/) (2015)
- [Flexibly Fair Representation Learning by Disentanglement](https://mlsanity.com/paper/1906.02589/) (2019)
- [Censoring Representations with an Adversary](https://mlsanity.com/paper/1511.05897/) (2015)
- [Say No to the Discrimination: Learning Fair Graph Neural Networks with Limited Sensitive Attribute Information](https://mlsanity.com/paper/2009.01454/) (2020)
- [Data Decisions and Theoretical Implications when Adversarially Learning Fair Representations](https://mlsanity.com/paper/1707.00075/) (2017)
- [FairGAN: Fairness-aware Generative Adversarial Networks](https://mlsanity.com/paper/1805.11202/) (2018)
- [On the Fairness of Disentangled Representations](https://mlsanity.com/paper/1905.13662/) (2019)
- [Fairness in Recommendation Ranking through Pairwise Comparisons](https://mlsanity.com/paper/1903.00780/) (2019)
- [Multisided Fairness for Recommendation](https://mlsanity.com/paper/1707.00093/) (2017)
- [Operationalizing Individual Fairness with Pairwise Fair Representations](https://mlsanity.com/paper/1907.01439/) (2019)
- [Fairness GAN](https://mlsanity.com/paper/1805.09910/) (2018)
- [Achieving non-discrimination in data release](https://mlsanity.com/paper/1611.07438/) (2016)

## Built on by (most cited first, 30 of 45)

- [Bias Mitigation for Machine Learning Classifiers: A Comprehensive Survey](https://mlsanity.com/paper/2207.07068/) (2022)
- [Improving Fairness in Graph Neural Networks via Mitigating Sensitive Attribute Leakage](https://mlsanity.com/paper/2206.03426/) (2022)
- [Improving Fairness in Deepfake Detection](https://mlsanity.com/paper/2306.16635/) (2023)
- [The Impossibility of Fair LLMs](https://mlsanity.com/paper/2406.03198/) (2024)
- [The Devil is in the Data: Learning Fair Graph Neural Networks via Partial Knowledge Distillation](https://mlsanity.com/paper/2311.17373/) (2023)
- [Fairness Without Harm: An Influence-Guided Active Sampling Approach](https://mlsanity.com/paper/2402.12789/) (2024)
- [BeMap: Balanced Message Passing for Fair Graph Neural Network](https://mlsanity.com/paper/2306.04107/) (2023)
- [Discover and Mitigate Multiple Biased Subgroups in Image Classifiers](https://mlsanity.com/paper/2403.12777/) (2024)
- [Achieve Fairness without Demographics for Dermatological Disease Diagnosis](https://mlsanity.com/paper/2401.08066/) (2024)
- [Distributionally Generative Augmentation for Fair Facial Attribute Classification](https://mlsanity.com/paper/2403.06606/) (2024)
- [Fairness without Sensitive Attributes via Knowledge Sharing](https://mlsanity.com/paper/2409.18470/) (2024)
- [Unveiling the Impact of Local Homophily on GNN Fairness: In-Depth Analysis and New Benchmarks](https://mlsanity.com/paper/2410.04287/) (2024)
- [Estimating and Controlling for Equalized Odds via Sensitive Attribute Predictors](https://mlsanity.com/paper/2207.12497/) (2022)
- [Fair Streaming Principal Component Analysis: Statistical and Algorithmic Viewpoint](https://mlsanity.com/paper/2310.18593/) (2023)
- [Fairness Under Demographic Scarce Regime](https://mlsanity.com/paper/2307.13081/) (2023)
- [Fairness without Demographics through Learning Graph of Gradients](https://mlsanity.com/paper/2412.03706/) (2024)
- [Hyper-parameter Tuning for Fair Classification without Sensitive Attribute Access](https://mlsanity.com/paper/2302.01385/) (2023)
- [Referee-Meta-Learning for Fast Adaptation of Locational Fairness](https://mlsanity.com/paper/2402.13379/) (2024)
- [On the Maximal Local Disparity of Fairness-Aware Classifiers](https://mlsanity.com/paper/2406.03255/) (2024)
- [Fair Text Classification via Transferable Representations](https://mlsanity.com/paper/2503.07691/) (2025)
- [Machine Learning with Multitype Protected Attributes: Intersectional Fairness through Regularisation](https://mlsanity.com/paper/2509.08163/) (2025)
- [How does overparametrization affect performance on minority groups?](https://mlsanity.com/paper/2206.03515/) (2022)
- [Trading-off price for data quality to achieve fair online allocation](https://mlsanity.com/paper/2306.13440/) (2023)
- [MAPPING: Debiasing Graph Neural Networks for Fair Node Classification with Limited Sensitive Information Leakage](https://mlsanity.com/paper/2401.12824/) (2024)
- [Enhancing Fairness and Performance in Machine Learning Models: A Multi-Task Learning Approach with Monte-Carlo Dropout and Pareto Optimality](https://mlsanity.com/paper/2404.08230/) (2024)
- [Fair Recommendations with Limited Sensitive Attributes: A Distributionally Robust Optimization Approach](https://mlsanity.com/paper/2405.01063/) (2024)
- [Towards Fair Graph Neural Networks via Graph Counterfactual without Sensitive Attributes](https://mlsanity.com/paper/2412.09947/) (2024)
- [Multiaccuracy and Multicalibration via Proxy Groups](https://mlsanity.com/paper/2503.02870/) (2025)
- [Towards Assumption-free Bias Mitigation](https://mlsanity.com/paper/2307.04105/) (2023)
- [Privacy Constrained Fairness Estimation for Decision Trees](https://mlsanity.com/paper/2312.08413/) (2023)

## Abstract (arXiv)

Despite the rapid development and great success of machine learning models, extensive studies have exposed their disadvantage of inheriting latent discrimination and societal bias from the training data. This phenomenon hinders their adoption on high-stake applications. Thus, many efforts have been taken for developing fair machine learning models. Most of them require that sensitive attributes are available during training to learn fair models. However, in many real-world applications, it is usually infeasible to obtain the sensitive attributes due to privacy or legal issues, which challenges existing fair-ensuring strategies. Though the sensitive attribute of each data sample is unknown, we observe that there are usually some non-sensitive features in the training data that are highly correlated with sensitive attributes, which can be used to alleviate the bias. Therefore, in this paper, we study a novel problem of exploring features that are highly correlated with sensitive attributes for learning fair and accurate classifiers. We theoretically show that by minimizing the correlation between these related features and model prediction, we can learn a fair classifier. Based on this motivation, we propose a novel framework which simultaneously uses these related features for accurate prediction and enforces fairness. In addition, the model can dynamically adjust the regularization weight of each related feature to balance its contribution on model classification and fairness. Experimental results on real-world datasets demonstrate the effectiveness of the proposed model for learning fair models with high classification accuracy.