论文标题
部分可观测时空混沌系统的无模型预测
Fair and Optimal Classification via Post-Processing
论文作者
论文摘要
为了减轻机器学习模型所表现出的偏见，可以将公平标准整合到培训过程中，以确保所有人口统计学的公平治疗，但通常以模型性能为代价。因此，了解这种权衡是公平算法的设计。为此，本文在最一般的多组，多级和嘈杂的环境下，对分类问题的人口统计学奇偶校验的固有权衡提供了完整的表征。具体而言，我们表明，通过随机和属性感知的公平分类器可实现的最小错误率是由wasserstein----- barycenter问题的最佳值给出的。从实际方面来说，我们的发现导致了一种简单的后处理算法，该算法从分数函数中得出公平分类器，当得分是最佳分数时，该算法会产生最佳的公平分类器。我们为我们的算法提供次优的分析和样品复杂性，并证明其在基准数据集上的有效性。
To mitigate the bias exhibited by machine learning models, fairness criteria can be integrated into the training process to ensure fair treatment across all demographics, but it often comes at the expense of model performance. Understanding such tradeoffs, therefore, underlies the design of fair algorithms. To this end, this paper provides a complete characterization of the inherent tradeoff of demographic parity on classification problems, under the most general multi-group, multi-class, and noisy setting. Specifically, we show that the minimum error rate achievable by randomized and attribute-aware fair classifiers is given by the optimal value of a Wasserstein-barycenter problem. On the practical side, our findings lead to a simple post-processing algorithm that derives fair classifiers from score functions, which yields the optimal fair classifier when the score is Bayes optimal. We provide suboptimality analysis and sample complexity for our algorithm, and demonstrate its effectiveness on benchmark datasets.
