论文标题
帕累托边境的机器学习的公平数据表示
Fair Data Representation for Machine Learning at the Pareto Frontier
论文作者
论文摘要
随着机器学习动力的决策在我们的日常生活中变得越来越重要，必须在基础数据处理中努力公平。我们为通过公平数据表示的预处理算法提出了一种预处理的算法，该算法的监督学习导致对预测误差和统计差异之间的帕累托前沿估计。尤其是，本工作应用最佳仿射传输来接近最佳公平$ l^2 $ - 目标监督学习的后处理后处理的后处理。此外，我们表明，从条件的（敏感信息）分布到其barycenter的Wasserstein-2大地测量学分布在$ l^2 $ -LOSS和平均敏感群体中的平均Pairwise wasserstein-2距离之间的帕累托前沿特征在学习成果上。数值模拟强调了优点：（1）预处理步骤是组合的，具有任意条件期望估计的监督学习方法和看不见的数据； （2）公平表示通过限制剩余数据相对于敏感数据的推断能力来保护敏感信息； （3）即使对于高维数据，最佳仿射图在计算上也是有效的。
As machine learning powered decision-making becomes increasingly important in our daily lives, it is imperative to strive for fairness in the underlying data processing. We propose a pre-processing algorithm for fair data representation via which supervised learning results in estimations of the Pareto frontier between prediction error and statistical disparity. Particularly, the present work applies the optimal affine transport to approach the post-processing Wasserstein-2 barycenter characterization of the optimal fair $L^2$-objective supervised learning via a pre-processing data deformation. Furthermore, we show that the Wasserstein-2 geodesics from the conditional (on sensitive information) distributions of the learning outcome to their barycenter characterizes the Pareto frontier between $L^2$-loss and the average pairwise Wasserstein-2 distance among sensitive groups on the learning outcome. Numerical simulations underscore the advantages: (1) the pre-processing step is compositive with arbitrary conditional expectation estimation supervised learning methods and unseen data; (2) the fair representation protects the sensitive information by limiting the inference capability of the remaining data with respect to the sensitive data; (3) the optimal affine maps are computationally efficient even for high-dimensional data.
