Fair classification is a critical challenge that has gained increasing importance due to international regulations and its growing use in high-stakes decision-making settings. Existing methods often rely on adversarial learning or distribution matching across sensitive groups; however, adversarial learning can be unstable, and distribution matching can be computationally intensive. To address these limitations, we propose a novel approach based on the characteristic function distance. Our method ensures that the learned representation contains minimal sensitive information while maintaining high effectiveness for downstream tasks. By utilizing characteristic functions, we achieve a more stable and efficient solution compared to traditional methods. Additionally, we introduce a simple relaxation of the objective function that guarantees fairness in common classification models with no performance degradation. Experimental results on benchmark datasets demonstrate that our approach consistently matches or achieves better fairness and predictive accuracy than existing methods. Moreover, our method maintains robustness and computational efficiency, making it a practical solution for real-world applications.

翻译：公平分类是一个关键挑战，由于国际法规及其在高风险决策场景中日益增长的应用，其重要性不断提升。现有方法通常依赖于跨敏感群体的对抗学习或分布匹配；然而，对抗学习可能不稳定，而分布匹配可能计算密集。为应对这些局限，我们提出了一种基于特征函数距离的新方法。我们的方法确保学习到的表示包含最少的敏感信息，同时保持对下游任务的高效性。通过利用特征函数，相比传统方法，我们实现了一种更稳定高效的解决方案。此外，我们引入了目标函数的一个简单松弛形式，可在常见分类模型中保证公平性且无性能损失。在基准数据集上的实验结果表明，我们的方法在公平性和预测准确性方面始终匹配或优于现有方法。此外，我们的方法保持了鲁棒性和计算效率，使其成为实际应用的实用解决方案。