FARF: A Fair and Adaptive Random Forests Classifier

Wenbin Zhang, Albert Bifet, Xiangliang Zhang, Jeremy C. Weiss, Wolfgang Nejdl

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

As Artificial Intelligence (AI) is used in more applications, the need to consider and mitigate biases from the learned models has followed. Most works in developing fair learning algorithms focus on the offline setting. However, in many real-world applications data comes in an online fashion and needs to be processed on the fly. Moreover, in practical application, there is a trade-off between accuracy and fairness that needs to be accounted for, but current methods often have multiple hyper-parameters with non-trivial interaction to achieve fairness. In this paper, we propose a flexible ensemble algorithm for fair decision-making in the more challenging context of evolving online settings. This algorithm, called FARF (Fair and Adaptive Random Forests), is based on using online component classifiers and updating them according to the current distribution, that also accounts for fairness and a single hyper-parameters that alters fairness-accuracy balance. Experiments on real-world discriminated data streams demonstrate the utility of FARF.
Original languageEnglish (US)
Title of host publicationAdvances in Knowledge Discovery and Data Mining
PublisherSpringer International Publishing
Pages245-256
Number of pages12
ISBN (Print)9783030757649
DOIs
StatePublished - May 8 2021

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