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Fairness assessment in algorithmic decisions that affect individuals, such as credit scoring, often relies on parity measures calculated at the aggregate group level. Such measures may not reveal which individuals experience unfairness or which explanatory factors contribute to it. In this paper, we propose a rank-base...
The proposed generative model and evaluation framework establish a structured basis for both enhancing and evaluating synthetic data under class imbalance and proposes a multi-view fidelity framework that integrates distributional, topological, complexity-based, and hardness-based perspectives to capture complementary...
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