Abstract
There has been a significant recent interest in algorithmic fairness within data-driven systems. In this paper, we consider group fairness within Case-based Reasoning. Group fairness targets to ensure parity of outcomes across pre-specified sensitive groups, defined on the basis of extant entrenched discrimination. Addressing the context of binary decision choice scenarios over binary sensitive attributes, we develop three separate fairness interventions that operate at different stages of the CBR process. These techniques, called Label Flipping (LF), Case Weighting (CW) and Weighted Adaptation (WA), use distinct strategies to enhance group fairness in CBR decision making. Through an extensive empirical evaluation over several popular datasets and against natural baseline methods, we show that our methods are able to achieve significant enhancements in fairness at low detriment to accuracy, thus illustrating effectiveness of our methods at advancing fairness.
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While our separate methods were designed for fairness interventions at different stages of the CBR process, it would be interesting to understand the complementarity between these methods and exploit them for application in scenarios where the user has control over all stages of the CBR process. We are considering extending this to cover multi-choice and structured decision scenarios and multi-valued sensitive attributes.
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Mitra, S., Mathew, D., P., D., Chakraborti, S. (2023). Group Fairness in Case-Based Reasoning. In: Massie, S., Chakraborti, S. (eds) Case-Based Reasoning Research and Development. ICCBR 2023. Lecture Notes in Computer Science(), vol 14141. Springer, Cham. https://doi.org/10.1007/978-3-031-40177-0_14
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