Flexible and Adaptive Fairness-aware Learning in Non-stationary Data Streams
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Zhang, Wenbin; Zhang, Mingli; Zhang, Ji; Liu, Zhen; Chen, Zhiyuan; Wang, Jianwu; Raff, Edward; Messina, Enza; Flexible and Adaptive Fairness-aware Learning in Non-stationary Data Streams; 32th International Conference on Tools with Artificial Intelligence;
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© 2020 IEEE Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
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Artificial intelligence (AI)-based decision-making systems are employed nowadays in an ever growing number of online as well as offline services–some of great importance. Depending on sophisticated learning algorithms and available data, these systems are increasingly becoming automated and data-driven. However, these systems can impact individuals and communities with ethical or legal consequences. Numerous approaches have therefore been proposed to develop decision making systems that are discrimination-conscious by-design. However, these methods assume the underlying data distribution is stationary without drift, which is counterfactual in many real world applications. In addition, their focus has been largely on minimizing discrimination while maximizing prediction performance without necessary flexibility in customizing the tradeoff according to different applications. To this end, we propose a learning algorithm for fair classification that also adapts to evolving data streams and further allows for a flexible control on the degree of accuracy and fairness. The positive results on a set of discriminated and non-stationary data streams demonstrate the effectiveness and flexibility of this approach.