To mitigate the effects of undesired biases in models, several approaches propose to pre-process the input dataset to reduce the risks of discrimination by preventing the inference of sensitive attributes. Unfortunately, most of these pre-processing methods lead to the generation a new distribution that is very different from the original one, thus often leading to unrealistic data. As a side effect, this new data distribution implies that existing models need to be re-trained to be able to make accurate predictions. To address this issue, we propose a novel pre-processing method, that we coin as fair mapping, based on the transformation of the distribution of protected groups onto a chosen target one, with additional privacy constraints whose objective is to prevent the inference of sensitive attributes. More precisely, we leverage on the recent works of the Wasserstein GAN and AttGAN frameworks to achieve the optimal transport of data points coupled with a discriminator enforcing the protection against attribute inference. Our proposed approach, preserves the interpretability of data and can be used without defining exactly the sensitive groups. In addition, our approach can be specialized to model existing state-of-the-art approaches, thus proposing a unifying view on these methods. Finally, several experiments on real and synthetic datasets demonstrate that our approach is able to hide the sensitive attributes, while limiting the distortion of the data and improving the fairness on subsequent data analysis tasks.

翻译：为减轻模型中非期望偏差的影响，多种方法提出对输入数据集进行预处理，通过阻止敏感属性的推断来降低歧视风险。然而，大多数预处理方法会产生与原始分布差异显著的新数据分布，往往导致数据不切实际。这种新数据分布产生的副作用是，现有模型需要重新训练才能进行准确预测。针对这一问题，我们提出一种新型预处理方法——公平映射，其核心思想是将受保护群体的分布变换到选定的目标分布，同时附加隐私约束以防止敏感属性推断。具体而言，我们借鉴Wasserstein GAN和AttGAN框架的最新研究成果，实现数据点的最优传输，并耦合判别器以强化对属性推断的防护。该方法既能保持数据的可解释性，又无需精确定义敏感群体。此外，该方法可通过特例化建模现有最先进方法，从而提出这些方法的统一视角。最后，在真实数据集和合成数据集上的多项实验表明，该方法能在限制数据失真并提升后续数据分析任务公平性的同时有效隐藏敏感属性。