发表机构
Univ. Grenoble Alpes, Inria, CNRS, Grenoble INP, LJK（格勒诺布尔大学、法国国家信息与自动化研究所、法国国家科学研究中心、格勒诺布尔国立综合理工学院、数值计算与知识工程实验室）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
研究连续高维敏感属性时的公平性问题，通过DPVar考虑平均人口统计学均等，提出FBO和ITD算法解决函数式双层问题，在合成数据等上表现优。
AI 中文摘要
当敏感属性是连续且高维的——人口统计得分向量、属性后验、年龄或收入概况——强制完全统计独立性通常过于严格，现有松弛依赖间接依赖惩罚或对抗方案，未直接针对公平性-准确性权衡。我们通过DPVar（给定敏感属性的条件均值预测的方差）考虑平均人口统计学均等，并表明优化它会产生一个函数式双层问题。我们针对此问题提出两种算法：FBO，它使用我们为平方损失情况推导的闭式伴随来获得精确的超梯度；ITD，它通过展开的内部步骤进行微分并扩展到平方损失之外。在合成数据和由60个表格回归数据集构建的新半合成基准上，两种方法都实现了最低或接近最低的总体公平性-准确性遗憾，并且始终匹配或优于强大的HSIC、对抗、线性依赖和广义DP基线。
英文摘要
We focus on regression settings where the fairness goal is to equalize average predictions across values of a sensitive attribute, a criterion known as mean demographic parity. Directly optimizing this criterion is difficult because it depends on a conditional mean that is unknown and changes during training. Common dependence penalties and adversarial methods do not estimate this conditional mean; instead, they push predictions toward full independence. This stronger constraint can reduce accuracy even when average predictions are already equal. Existing conditional-mean methods are limited to linear predictors or low-dimensional sensitive attributes. We enable direct optimization of mean demographic parity using DPVar, a fairness measure defined as the variance of the conditional mean prediction. Because the conditional mean must be estimated as the predictor changes, optimizing DPVar leads to a functional bilevel problem. We develop two solvers: FBO, which uses a closed-form hypergradient, and an iterative-differentiation (ITD) solver that differentiates through updates of the conditional-mean estimator. Unlike previous conditional-mean methods, our approach applies to nonlinear predictors and high-dimensional continuous sensitive attributes. Across a semi-synthetic benchmark built from 21 tabular regression datasets and Communities & Crime data, FBO and ITD recover competitive or better accuracy-DPVar trade-offs than existing methods.
