# Fair-SSL: Building fair ML Software with less data

arXiv: https://arxiv.org/abs/2111.02038 · HTML: https://mlsanity.com/paper/2111.02038/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 21 of its 69 references that are in the map.
- First posted on arXiv 3 Nov 2021.

## Builds on (most cited first, 21 of 21)

- [Equality of Opportunity in Supervised Learning](https://mlsanity.com/paper/1610.02413/) (2016)
- [Semantics derived automatically from language corpora contain human-like biases](https://mlsanity.com/paper/1608.07187/) (2016)
- [Mitigating Unwanted Biases with Adversarial Learning](https://mlsanity.com/paper/1801.07593/) (2018)
- [Inherent Trade-Offs in the Fair Determination of Risk Scores](https://mlsanity.com/paper/1609.05807/) (2016)
- [AI Fairness 360: An Extensible Toolkit for Detecting, Understanding, and Mitigating Unwanted Algorithmic Bias](https://mlsanity.com/paper/1810.01943/) (2018)
- [Fairness in Criminal Justice Risk Assessments: The State of the Art](https://mlsanity.com/paper/1703.09207/) (2017)
- [Identifying and Correcting Label Bias in Machine Learning](https://mlsanity.com/paper/1901.04966/) (2019)
- [Why Is My Classifier Discriminatory?](https://mlsanity.com/paper/1805.12002/) (2018)
- [Fairness Testing: Testing Software for Discrimination](https://mlsanity.com/paper/1709.03221/) (2017)
- [Bias in Machine Learning Software: Why? How? What to do?](https://mlsanity.com/paper/2105.12195/) (2021)
- [Automated Directed Fairness Testing](https://mlsanity.com/paper/1807.00468/) (2018)
- [A causal framework for discovering and removing direct and indirect discrimination](https://mlsanity.com/paper/1611.07509/) (2016)
- [Do the Machine Learning Models on a Crowd Sourced Platform Exhibit Bias? An Empirical Study on Model Fairness](https://mlsanity.com/paper/2005.12379/) (2020)
- [Class-Imbalanced Semi-Supervised Learning](https://mlsanity.com/paper/2002.06815/) (2020)
- [Making Fair ML Software using Trustworthy Explanation](https://mlsanity.com/paper/2007.02893/) (2020)
- [Fairness in Semi-supervised Learning: Unlabeled Data Help to Reduce Discrimination](https://mlsanity.com/paper/2009.12040/) (2020)
- [Software Engineering for Fairness: A Case Study with Hyperparameter Optimization](https://mlsanity.com/paper/1905.05786/) (2019)
- [Better Data Labelling with EMBLEM (and how that Impacts Defect Prediction)](https://mlsanity.com/paper/1905.01719/) (2019)
- [Improving Vulnerability Inspection Efficiency Using Active Learning](https://mlsanity.com/paper/1803.06545/) (2018)
- [FRUGAL: Unlocking SSL for Software Analytics](https://mlsanity.com/paper/2108.09847/) (2021)
- [TERMINATOR: Better Automated UI Test Case Prioritization](https://mlsanity.com/paper/1905.07019/) (2019)

## Built on by (most cited first, 4 of 4)

- [Bias Mitigation for Machine Learning Classifiers: A Comprehensive Survey](https://mlsanity.com/paper/2207.07068/) (2022)
- [FairSSL: Fair Multimodal Self-Supervised Learning](https://mlsanity.com/paper/2508.16748/) (2025)
- [Contexts Matter: An Empirical Study on Contextual Influence in Fairness Testing for Deep Learning Systems](https://mlsanity.com/paper/2408.06102/) (2024)
- [Applications and Challenges of Fairness APIs in Machine Learning Software](https://mlsanity.com/paper/2508.16377/) (2025)

## Abstract (arXiv)

Ethical bias in machine learning models has become a matter of concern in the software engineering community. Most of the prior software engineering works concentrated on finding ethical bias in models rather than fixing it. After finding bias, the next step is mitigation. Prior researchers mainly tried to use supervised approaches to achieve fairness. However, in the real world, getting data with trustworthy ground truth is challenging and also ground truth can contain human bias. Semi-supervised learning is a machine learning technique where, incrementally, labeled data is used to generate pseudo-labels for the rest of the data (and then all that data is used for model training). In this work, we apply four popular semi-supervised techniques as pseudo-labelers to create fair classification models. Our framework, Fair-SSL, takes a very small amount (10%) of labeled data as input and generates pseudo-labels for the unlabeled data. We then synthetically generate new data points to balance the training data based on class and protected attribute as proposed by Chakraborty et al. in FSE 2021. Finally, the classification model is trained on the balanced pseudo-labeled data and validated on test data. After experimenting on ten datasets and three learners, we find that Fair-SSL achieves similar performance as three state-of-the-art bias mitigation algorithms. That said, the clear advantage of Fair-SSL is that it requires only 10% of the labeled training data. To the best of our knowledge, this is the first SE work where semi-supervised techniques are used to fight against ethical bias in SE ML models.