# Online Fairness-Aware Learning with Imbalanced Data Streams

arXiv: https://arxiv.org/abs/2108.06231 · HTML: https://mlsanity.com/paper/2108.06231/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 40 references that are in the map.
- First posted on arXiv 13 Aug 2021.

## Builds on (most cited first, 10 of 10)

- [Equality of Opportunity in Supervised Learning](https://mlsanity.com/paper/1610.02413/) (2016)
- [Automated Experiments on Ad Privacy Settings: A Tale of Opacity, Choice, and Discrimination](https://mlsanity.com/paper/1408.6491/) (2014)
- [A Confidence-Based Approach for Balancing Fairness and Accuracy](https://mlsanity.com/paper/1601.05764/) (2016)
- [FAHT: An Adaptive Fairness-aware Decision Tree Classifier](https://mlsanity.com/paper/1907.07237/) (2019)
- [AdaFair: Cumulative Fairness Adaptive Boosting](https://mlsanity.com/paper/1909.08982/) (2019)
- [When Will AI Exceed Human Performance? Evidence from AI Experts](https://mlsanity.com/paper/1705.08807/) (2017)
- [FAE: A Fairness-Aware Ensemble Framework](https://mlsanity.com/paper/2002.00695/) (2020)
- [Fairness-enhancing interventions in stream classification](https://mlsanity.com/paper/1907.07223/) (2019)
- [Online Ensemble Learning for Imbalanced Data Streams](https://mlsanity.com/paper/1310.8004/) (2013)
- [FairNN- Conjoint Learning of Fair Representations for Fair Decisions](https://mlsanity.com/paper/2004.02173/) (2020)

## Built on by (most cited first, 8 of 8)

- [A survey on learning from imbalanced data streams: taxonomy, challenges, empirical study, and reproducible experimental framework](https://mlsanity.com/paper/2204.03719/) (2022)
- [Preventing Discriminatory Decision-making in Evolving Data Streams](https://mlsanity.com/paper/2302.08017/) (2023)
- [ABCinML: Anticipatory Bias Correction in Machine Learning Applications](https://mlsanity.com/paper/2206.06960/) (2022)
- [Parity-based Cumulative Fairness-aware Boosting](https://mlsanity.com/paper/2201.01148/) (2022)
- [AdaCC: Cumulative Cost-Sensitive Boosting for Imbalanced Classification](https://mlsanity.com/paper/2209.08309/) (2022)
- [Continuous Management of Machine Learning-Based Application Behavior](https://mlsanity.com/paper/2311.12686/) (2023)
- [Peer-induced Fairness: A Causal Approach for Algorithmic Fairness Auditing](https://mlsanity.com/paper/2408.02558/) (2024)
- [Continuous Fair SMOTE -- Fairness-Aware Stream Learning from Imbalanced Data](https://mlsanity.com/paper/2505.13116/) (2025)

## Abstract (arXiv)

Data-driven learning algorithms are employed in many online applications, in which data become available over time, like network monitoring, stock price prediction, job applications, etc. The underlying data distribution might evolve over time calling for model adaptation as new instances arrive and old instances become obsolete. In such dynamic environments, the so-called data streams, fairness-aware learning cannot be considered as a one-off requirement, but rather it should comprise a continual requirement over the stream. Recent fairness-aware stream classifiers ignore the problem of class imbalance, which manifests in many real-life applications, and mitigate discrimination mainly because they "reject" minority instances at large due to their inability to effectively learn all classes. In this work, we propose ours, an online fairness-aware approach that maintains a valid and fair classifier over the stream. ours~is an online boosting approach that changes the training distribution in an online fashion by monitoring stream's class imbalance and tweaks its decision boundary to mitigate discriminatory outcomes over the stream. Experiments on 8 real-world and 1 synthetic datasets from different domains with varying class imbalance demonstrate the superiority of our method over state-of-the-art fairness-aware stream approaches with a range (relative) increase [11.2%-14.2%] in balanced accuracy, [22.6%-31.8%] in gmean, [42.5%-49.6%] in recall, [14.3%-25.7%] in kappa and [89.4%-96.6%] in statistical parity (fairness).