# Fairness in Semi-supervised Learning: Unlabeled Data Help to Reduce Discrimination

arXiv: https://arxiv.org/abs/2009.12040 · HTML: https://mlsanity.com/paper/2009.12040/

## Key facts

- Cited by 22 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 25 Sep 2020.

## Built on by (most cited first, 22 of 22)

- [Transferring Fairness under Distribution Shifts via Fair Consistency Regularization](https://mlsanity.com/paper/2206.12796/) (2022)
- [Modeling Techniques for Machine Learning Fairness: A Survey](https://mlsanity.com/paper/2111.03015/) (2021)
- [Don't Throw it Away! The Utility of Unlabeled Data in Fair Decision Making](https://mlsanity.com/paper/2205.04790/) (2022)
- [A novel approach for Fair Principal Component Analysis based on eigendecomposition](https://mlsanity.com/paper/2208.11362/) (2022)
- [Exploring Bias and Prediction Metrics to Characterise the Fairness of Machine Learning for Equity-Centered Public Health Decision-Making: A Narrative Review](https://mlsanity.com/paper/2408.13295/) (2024)
- [Semi-FairVAE: Semi-supervised Fair Representation Learning with Adversarial Variational Autoencoder](https://mlsanity.com/paper/2204.00536/) (2022)
- [Bipartite Ranking Fairness through a Model Agnostic Ordering Adjustment](https://mlsanity.com/paper/2307.14668/) (2023)
- [Fair-SSL: Building fair ML Software with less data](https://mlsanity.com/paper/2111.02038/) (2021)
- [A Catalog of Fairness-Aware Practices in Machine Learning Engineering](https://mlsanity.com/paper/2408.16683/) (2024)
- [A Comprehensive Survey and Classification of Evaluation Criteria for Trustworthy Artificial Intelligence](https://mlsanity.com/paper/2410.17281/) (2024)
- [Algorithmic Fairness: A Tolerance Perspective](https://mlsanity.com/paper/2405.09543/) (2024)
- [AFed: Algorithmic Fair Federated Learning](https://mlsanity.com/paper/2501.02732/) (2025)
- [Do Fairness Interventions Come at the Cost of Privacy: Evaluations for Binary Classifiers](https://mlsanity.com/paper/2503.06150/) (2025)
- [Learning to Fairly Classify the Quality of Wireless Links](https://mlsanity.com/paper/2102.11655/) (2021)
- [Fair Representation Learning using Interpolation Enabled Disentanglement](https://mlsanity.com/paper/2108.00295/) (2021)
- [Towards Better Fairness-Utility Trade-off: A Comprehensive Measurement-Based Reinforcement Learning Framework](https://mlsanity.com/paper/2307.11379/) (2023)
- [Boosting Fair Classifier Generalization through Adaptive Priority Reweighing](https://mlsanity.com/paper/2309.08375/) (2023)
- [Efficient Edge Rewiring Strategies for Enhancing PageRank Fairness](https://mlsanity.com/paper/2602.02512/) (2026)
- [Semi-supervised Active Regression](https://mlsanity.com/paper/2106.06676/) (2021)
- [Alpha and Prejudice: Improving α-sized Worst-case Fairness via Intrinsic Reweighting](https://mlsanity.com/paper/2411.03068/) (2024)
- [Aggregation-aware MLP: An Unsupervised Approach for Graph Message-passing](https://mlsanity.com/paper/2507.20127/) (2025)
- [FairAD: Computationally Efficient Fair Graph Clustering via Algebraic Distance](https://mlsanity.com/paper/2510.27136/) (2025)

## Abstract (arXiv)

A growing specter in the rise of machine learning is whether the decisions made by machine learning models are fair. While research is already underway to formalize a machine-learning concept of fairness and to design frameworks for building fair models with sacrifice in accuracy, most are geared toward either supervised or unsupervised learning. Yet two observations inspired us to wonder whether semi-supervised learning might be useful to solve discrimination problems. First, previous study showed that increasing the size of the training set may lead to a better trade-off between fairness and accuracy. Second, the most powerful models today require an enormous of data to train which, in practical terms, is likely possible from a combination of labeled and unlabeled data. Hence, in this paper, we present a framework of fair semi-supervised learning in the pre-processing phase, including pseudo labeling to predict labels for unlabeled data, a re-sampling method to obtain multiple fair datasets and lastly, ensemble learning to improve accuracy and decrease discrimination. A theoretical decomposition analysis of bias, variance and noise highlights the different sources of discrimination and the impact they have on fairness in semi-supervised learning. A set of experiments on real-world and synthetic datasets show that our method is able to use unlabeled data to achieve a better trade-off between accuracy and discrimination.