The biases and discrimination of machine learning algorithms have attracted significant attention, leading to the development of various algorithms tailored to specific contexts. However, these solutions often fall short of addressing fairness issues inherent in machine learning operations. In this paper, we present a debiasing framework designed to find an optimal fair transformation of input data that maximally preserves data predictability. A distinctive feature of our approach is its flexibility and efficiency. It can be integrated with any downstream black-box classifiers, providing continuous fairness guarantees with minimal retraining efforts, even in the face of frequent data drifts, evolving fairness requirements, and batches of similar tasks. To achieve this, we leverage the normalizing flows to enable efficient, information-preserving data transformation, ensuring that no critical information is lost during the debiasing process. Additionally, we incorporate the Wasserstein distance as the unfairness measure to guide the optimization of data transformations. Finally, we introduce an efficient optimization algorithm with closed-formed gradient computations, making our framework scalable and suitable for dynamic, real-world environments.

本文针对机器学习操作中的公平性问题，提出了一种去偏框架，旨在实现输入数据的最优公平变换，同时最大限度地保留数据的可预测性。该框架灵活高效，可与任何下游黑箱分类器集成，能够在频繁的数据漂移和变化的公平性要求下，提供持续的公平性保障。

AdapFair：确保机器学习操作的持续公平性