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arXiv Machine Learning By Zhouting Zhao, Tin Lok James Ng

Individual Fairness via Local Predictive Consistency

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The paper introduces a node‑level relaxation of individual fairness called local predictive consistency, where each prediction is compared to a similarity‑weighted average of predictions in its local neighborhood. This approach yields an interpretable fairness score and a regularized learning method based on local residuals, and it explicitly constructs the neighborhood graph with threshold selection guided by graph‑coverage diagnostics. The authors apply the method to binary classification, regression, and time‑to‑event prediction, and compare it with pairwise constraint‑based fairness formulations to analyze fairness‑performance trade‑offs.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

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