arXiv 论文提出 SAFER：用显著性感知正则化解决交叉公平性评估不稳定问题

论文提出 significant-excess 指标族，只衡量超出特定置信水平下抽样波动所能解释的那部分子组差异，并在此基础上构建 SAFER（Significance-Aware Fairness Regularizer），通过 soft-count 似然比证据统计量对公平性惩罚项做门控，且具备可证明的安全性：当所有子组差异远低于阈值时，无约束最优解对 SAFER 近乎平稳。

在三个基准数据集及专门构造的压力测试变体上，消融实验显示移除任一组件都会引发理论预测的失效；在稀疏交叉子组场景中，SAFER 取得比基线方法更优的公平性-效用 Pareto 前沿，且在无认证不公平的情况下保持效用不损失。

Overcoming the Resolution Limit: Significance-Aware Regularization for Intersectional Fairness

Intersectional fairness requires evaluating models across fine-grained subgroups. However, as group definitions become more granular, the sample size per subgroup shrinks, causing fairness estimates to become increasingly unstable. Existing in-processing methods are certainty-blind: they penalize observed disparities without distinguishing statistically certified unfairness from sampling noise. This leads to fairness overfitting, where models sacrifice utility to "fix" spurious disparities in sparse regimes. To address this, we introduce the significant-excess metric family, a continuous, uncertainty-aware fairness score that measures only the portion of a subgroup's disparity exceeding what sampling variation can explain at a specific confidence level. Building on this metric, we propose SAFER (Significance-Aware Fairness Regularizer), a differentiable in-processing method that gates fairness penalties using a soft-count likelihood-ratio evidence statistic. SAFER satisfies a provable safety property: when every subgroup disparity lies well below the gate threshold, the unconstrained optimum is nearly stationary for SAFER. At that optimum, the back-propagated contribution of its significance-gated component is exponentially small, and the additional fairness contribution comes from a small ungated term with a fixed weight. Mechanism ablations across three benchmark datasets and purpose-built stress variants show that removing any component of SAFER (threshold smoothing, the significance gate, or a dense-tail term) produces the failure the theory predicts on at least one stress variant. In sparse intersectional settings, SAFER achieves a better fairness-utility Pareto frontier than the baseline methods, and it preserves utility when certified unfairness is absent.

This report is categorized under AITOP's "Papers" desk. Within our cross-regional analytical framework, it directly maps to AI developer toolchains, coding agents, and engineering workflows. Key entities and technical topics under continuous tracking include SAFER, 公平性, 交叉公平, 正则化.

Source attribution: curated from arXiv cs.LG (@Antonio Ferrara, André Panisson, Francesco Cozzi, Alan Perotti, Francesco Bonchi).