发表机构
Indian Institute of Technology, Gandhinagar（印度理工学院甘地纳格尔分校）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对基于邻域的公平性审计的鲁棒性问题，提出几何框架分析其在有界扰动下的鲁棒性，引入审计波动性指标，实验验证框架可解释该类审计的观测稳定性。
AI 中文摘要
基于邻域的公平性审计通过比较特征空间中相似个体的预测结果来评估个体公平性，尽管其应用广泛，但审计程序本身的鲁棒性却鲜为人知。由于这类审计依赖最近邻关系，特征空间中的微小扰动会改变局部邻域，即便模型预测保持不变，也会产生不同的公平性评估结果。我们开发了一种几何框架，用于分析有界扰动下基于邻域的公平性审计的鲁棒性。我们的分析确立了邻域不变性的充分条件，量化了邻域替换如何传播至审计不稳定性，并引入了审计波动性这一指标，用于衡量重复扰动下公平性审计的预期敏感性。在基准数据集上开展的实验验证了该理论分析，且表明所提框架可解释基于邻域的公平性审计的观测稳定性。
英文摘要
Neighborhood-based fairness audits evaluate individual fairness by comparing predictions among similar individuals in feature space. Despite their widespread use, little is known about the robustness of the auditing procedure itself. Because these audits rely on nearest neighbor relationships, small perturbations in feature space can alter local neighborhoods and produce different fairness assessments even when model predictions remain unchanged. We develop a geometric framework for analyzing the robustness of neighborhood-based fairness audits under bounded perturbations. Our analysis establishes sufficient conditions for neighborhood invariance, quantifies how neighborhood replacement propagates to audit instability, and introduces audit volatility, a measure of the expected sensitivity of fairness audits under repeated perturbations. Experiments on benchmark datasets support the theoretical analysis and show that the proposed framework explains the observed stability of neighborhood-based fairness audits.
