Abstract
Machine learning has been widely applied to extract insights from streaming data. However, ethical issues such as fairness have emerged related to these decision-support systems. Feature engineering methods have shown potential in representing and learning of fairness learning. However, these techniques have not been applied to streaming data. In this paper, we proposed a fairness-aware swarm-based machine learning for streaming data. The novelty of this algorithm is in the utilisation of two swarms, one for classification by building a network of prototypes and one for discrimination mitigating using feature weighting. Experiments with well-known datasets in fairness learning show that the proposed methods can improve fairness while maintaining the classification performance.
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Pham, D., Tran, B., Nguyen, S., Alahakoon, D. (2022). Fairness Aware Swarm-based Machine Learning for Data Streams. In: Aziz, H., Corrêa, D., French, T. (eds) AI 2022: Advances in Artificial Intelligence. AI 2022. Lecture Notes in Computer Science(), vol 13728. Springer, Cham. https://doi.org/10.1007/978-3-031-22695-3_15
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