Fair and Optimal Classification via Post-Processing
It addresses fairness-accuracy tradeoffs in machine learning for classification tasks, providing a foundational theoretical framework and practical algorithm, though it is incremental as it builds on existing post-processing methods.
The paper tackles the tradeoff between fairness and accuracy in classification by characterizing the optimal error rate for demographic parity in multi-group, multi-class settings, showing it relates to a Wasserstein-barycenter problem, and introduces a post-processing algorithm that achieves optimal fairness with proven suboptimality and sample complexity, demonstrating effectiveness on benchmarks.
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.