# Fairness-enhancing interventions in stream classification

arXiv: https://arxiv.org/abs/1907.07223 · HTML: https://mlsanity.com/paper/1907.07223/

## Key facts

- Cited by 15 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Jul 2019.

## Built on by (most cited first, 15 of 15)

- [Bias Mitigation for Machine Learning Classifiers: A Comprehensive Survey](https://mlsanity.com/paper/2207.07068/) (2022)
- [FARF: A Fair and Adaptive Random Forests Classifier](https://mlsanity.com/paper/2108.07403/) (2021)
- [Supervised Algorithmic Fairness in Distribution Shifts: A Survey](https://mlsanity.com/paper/2402.01327/) (2024)
- [Preventing Discriminatory Decision-making in Evolving Data Streams](https://mlsanity.com/paper/2302.08017/) (2023)
- [Online Fairness-Aware Learning with Imbalanced Data Streams](https://mlsanity.com/paper/2108.06231/) (2021)
- [Algorithmic Fairness Generalization under Covariate and Dependence Shifts Simultaneously](https://mlsanity.com/paper/2311.13816/) (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)
- [A Survey on Preserving Fairness Guarantees in Changing Environments](https://mlsanity.com/paper/2211.07530/) (2022)
- [Unveiling Group-Specific Distributed Concept Drift: A Fairness Imperative in Federated Learning](https://mlsanity.com/paper/2402.07586/) (2024)
- [InSaAF: Incorporating Safety through Accuracy and Fairness | Are LLMs ready for the Indian Legal Domain?](https://mlsanity.com/paper/2402.10567/) (2024)
- [Discrimination and Class Imbalance Aware Online Naive Bayes](https://mlsanity.com/paper/2211.04812/) (2022)
- [Continuous Management of Machine Learning-Based Application Behavior](https://mlsanity.com/paper/2311.12686/) (2023)
- [Evolutionary Multi-Objective Optimisation for Fairness-Aware Self Adjusting Memory Classifiers in Data Streams](https://mlsanity.com/paper/2404.12076/) (2024)
- [A Retrospective of the Tutorial on Opportunities and Challenges of Online Deep Learning](https://mlsanity.com/paper/2405.17222/) (2024)

## Abstract (arXiv)

The wide spread usage of automated data-driven decision support systems has raised a lot of concerns regarding accountability and fairness of the employed models in the absence of human supervision. Existing fairness-aware approaches tackle fairness as a batch learning problem and aim at learning a fair model which can then be applied to future instances of the problem. In many applications, however, the data comes sequentially and its characteristics might evolve with time. In such a setting, it is counter-intuitive to "fix" a (fair) model over the data stream as changes in the data might incur changes in the underlying model therefore, affecting its fairness. In this work, we propose fairness-enhancing interventions that modify the input data so that the outcome of any stream classifier applied to that data will be fair. Experiments on real and synthetic data show that our approach achieves good predictive performance and low discrimination scores over the course of the stream.