用可解释SVD后处理，让神经网络回归模型更公平且不依赖敏感属性推理。
Post-processing for Fair Regression via Explainable SVD
- 通过变换权重矩阵，使SVD奇异值直接反映两组输出均值方差差异。
- 在满足统计公平性约束下，解析求解最优权重，实现公平与精度平衡。
- 适合需要公平性保障但不能在推理时使用敏感属性的场景。
本文提出一种后处理算法，用于训练满足统计平等性的神经网络回归模型。该方法利用权重矩阵的可解释奇异值分解（SVD），通过线性变换权重矩阵，使得变换后矩阵的奇异值直接对应于两组输出分布的一阶和二阶矩差异。由此可将公平性约束转化为对奇异值的约束，并在这些约束下解析求解最优权重。在多个数据集上的实验表明，本方法在无需在推理时使用敏感属性的情况下，实现了与基线相当或更优的公平性-准确性权衡。
原文摘要 · Abstract (English)
This paper presents a post-processing algorithm for training fair neural network regression models that satisfy statistical parity, utilizing an explainable singular value decomposition (SVD) of the weight matrix. We propose a linear transformation of the weight matrix, whereby the singular values derived from the SVD of the transformed matrix directly correspond to the differences in the first and second moments of the output distributions across two groups. Consequently, we can convert the fairness constraints into constraints on the singular values. We analytically solve the problem of finding the optimal weights under these constraints. Experimental validation on various datasets demonstrates that our method achieves a similar or superior fairness-accuracy trade-off compared to the baselines without using the sensitive attribute at the inference time.
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