Preventing Discriminatory Decision-making in Evolving Data Streams
Document Type
Conference Proceeding
Publication Date
6-2023
Department
Department of Computer Science; Department of Social Sciences
Abstract
Bias in machine learning has rightly received significant attention over the past decade. However, most fair machine learning (fair-ML) works to address bias in decision-making systems has focused solely on the offline setting. Despite the wide prevalence of online systems in the real world, work on identifying and correcting bias in the online setting is severely lacking. The unique challenges of the online environment make addressing bias more difficult than in the offline setting. First, Streaming Machine Learning (SML) algorithms must deal with the constantly evolving real-time data stream. Secondly, they need to adapt to changing data distributions (concept drift) to make accurate predictions on new incoming data. Incorporating fairness constraints into this already intricate task is not straightforward. In this work, we focus on the challenges of achieving fairness in biased data streams while accounting for the presence of concept drift, accessing one sample at a time. We present Fair Sampling over Stream (FS2), a novel fair rebalancing approach capable of being integrated with SML classification algorithms. Furthermore, we devise the first unified performance-fairness metric, Fairness Bonded Utility (FBU), to efficiently evaluate and compare the trade-offs between performance and fairness across various bias mitigation methods. FBU simplifies the comparison of fairness-performance trade-offs of multiple techniques through one unified and intuitive evaluation, allowing model designers to easily choose a technique. Overall, extensive evaluations show our measures surpass those of other fair online techniques previously reported in the literature.
Publication Title
ACM International Conference Proceeding Series
ISBN
9781450372527
Recommended Citation
Wang, Z.,
Saxena, N.,
Yu, T.,
Karki, S.,
Zetty, T.,
Haque, I.,
Zhou, S.,
KC, D.,
Stockwell, I.,
Wang, X.,
Bifet, A.,
&
Zhang, W.
(2023).
Preventing Discriminatory Decision-making in Evolving Data Streams.
ACM International Conference Proceeding Series, 149-159.
http://doi.org/10.1145/3593013.3593984
Retrieved from: https://digitalcommons.mtu.edu/michigantech-p/17388