Fairness in the Flow: An Overview and Drift-Aware Evaluation of Fair Classifiers for Drifting Data Streams

Kathrin Lammers, MaryBeth Defrance, Barbara Hammer, Valerie Vaquet
Proceedings of Fifth European Conference on Algorithmic Fairness, PMLR 350:84-100, 2026.

Abstract

Machine learning systems increasingly inform high-stakes decisions, making fairness, transparency, and reliability essential. While algorithmic fairness has been widely studied in the traditional batch setting, its extension to stream learning, where data arrives continuously, and distributions may evolve, remains underexplored. This paper surveys and systematizes the emerging field of fair stream learning, highlighting challenges such as fairness–performance trade-offs, temporal dynamics, and class imbalance. We provide a comprehensive overview of existing fair stream learning methods and propose a drift-aware evaluation framework. Our empirical analyses, for the first time, comprehensively utilize both drift-aware and cumulative score aggregation on streams with controlled and fairness-relevant drift. We compare representative approaches, offer insights into their behavior under evolving data conditions, and identify directions for future research.

Cite this Paper

BibTeX
@InProceedings{pmlr-v350-lammers26a, title = {Fairness in the Flow: An Overview and Drift-Aware Evaluation of Fair Classifiers for Drifting Data Streams}, author = {Lammers, Kathrin and Defrance, MaryBeth and Hammer, Barbara and Vaquet, Valerie}, booktitle = {Proceedings of Fifth European Conference on Algorithmic Fairness}, pages = {84--100}, year = {2026}, editor = {De Bie, Tijl and Defrance, MaryBeth and Calders, Toon and Nguyen, Dennis}, volume = {350}, series = {Proceedings of Machine Learning Research}, month = {02--04 Sep}, publisher = {PMLR}, pdf = {https://raw.githubusercontent.com/mlresearch/v350/main/assets/lammers26a/lammers26a.pdf}, url = {https://proceedings.mlr.press/v350/lammers26a.html}, abstract = {Machine learning systems increasingly inform high-stakes decisions, making fairness, transparency, and reliability essential. While algorithmic fairness has been widely studied in the traditional batch setting, its extension to stream learning, where data arrives continuously, and distributions may evolve, remains underexplored. This paper surveys and systematizes the emerging field of fair stream learning, highlighting challenges such as fairness–performance trade-offs, temporal dynamics, and class imbalance. We provide a comprehensive overview of existing fair stream learning methods and propose a drift-aware evaluation framework. Our empirical analyses, for the first time, comprehensively utilize both drift-aware and cumulative score aggregation on streams with controlled and fairness-relevant drift. We compare representative approaches, offer insights into their behavior under evolving data conditions, and identify directions for future research.} }
Endnote
%0 Conference Paper %T Fairness in the Flow: An Overview and Drift-Aware Evaluation of Fair Classifiers for Drifting Data Streams %A Kathrin Lammers %A MaryBeth Defrance %A Barbara Hammer %A Valerie Vaquet %B Proceedings of Fifth European Conference on Algorithmic Fairness %C Proceedings of Machine Learning Research %D 2026 %E Tijl De Bie %E MaryBeth Defrance %E Toon Calders %E Dennis Nguyen %F pmlr-v350-lammers26a %I PMLR %P 84--100 %U https://proceedings.mlr.press/v350/lammers26a.html %V 350 %X Machine learning systems increasingly inform high-stakes decisions, making fairness, transparency, and reliability essential. While algorithmic fairness has been widely studied in the traditional batch setting, its extension to stream learning, where data arrives continuously, and distributions may evolve, remains underexplored. This paper surveys and systematizes the emerging field of fair stream learning, highlighting challenges such as fairness–performance trade-offs, temporal dynamics, and class imbalance. We provide a comprehensive overview of existing fair stream learning methods and propose a drift-aware evaluation framework. Our empirical analyses, for the first time, comprehensively utilize both drift-aware and cumulative score aggregation on streams with controlled and fairness-relevant drift. We compare representative approaches, offer insights into their behavior under evolving data conditions, and identify directions for future research.
APA
Lammers, K., Defrance, M., Hammer, B. & Vaquet, V.. (2026). Fairness in the Flow: An Overview and Drift-Aware Evaluation of Fair Classifiers for Drifting Data Streams. Proceedings of Fifth European Conference on Algorithmic Fairness, in Proceedings of Machine Learning Research 350:84-100 Available from https://proceedings.mlr.press/v350/lammers26a.html.

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