Fairness holds a pivotal role in the realm of machine learning, particularly when it comes to addressing groups categorised by sensitive attributes, e.g., gender, race. Prevailing algorithms in fair learning predominantly hinge on accessibility or estimations of these sensitive attributes, at least in the training process. We design a single group-blind projection map that aligns the feature distributions of both groups in the source data, achieving (demographic) group parity, without requiring values of the protected attribute for individual samples in the computation of the map, as well as its use. Instead, our approach utilises the feature distributions of the privileged and unprivileged groups in a boarder population and the essential assumption that the source data are unbiased representation of the population. We present numerical results on synthetic data and real data.

翻译：公平性在机器学习领域具有关键作用，尤其是在处理基于敏感属性（如性别、种族）划分的群体时。公平学习中的主流算法主要依赖于这些敏感属性的可获取性或估计值（至少是在训练过程中）。我们设计了一种单一的群体盲投影映射，该映射能够对齐源数据中两个群体的特征分布，从而实现人口统计上的群体平价，且在此映射的计算及使用过程中无需个体样本的保护属性值。相反，我们的方法利用更广泛群体中特权群体与非特权群体的特征分布，并基于源数据是群体无偏表示这一核心假设。我们展示了合成数据与真实数据上的数值结果。