We consider the problem of producing fair probabilistic classifiers for multi-class classification tasks. We formulate this problem in terms of "projecting" a pre-trained (and potentially unfair) classifier onto the set of models that satisfy target group-fairness requirements. The new, projected model is given by post-processing the outputs of the pre-trained classifier by a multiplicative factor. We provide a parallelizable iterative algorithm for computing the projected classifier and derive both sample complexity and convergence guarantees. Comprehensive numerical comparisons with state-of-the-art benchmarks demonstrate that our approach maintains competitive performance in terms of accuracy-fairness trade-off curves, while achieving favorable runtime on large datasets. We also evaluate our method at scale on an open dataset with multiple classes, multiple intersectional protected groups, and over 1M samples.
翻译:我们考虑了为多级分类任务编制公平概率分类方法的问题。我们从“预测”一个事先经过训练的(可能不公平的)分类方法的角度,将这一问题写进符合目标群体公平要求的一组模型上。新的、预测的模式是用一个倍增因素处理预先训练分类者的产出。我们为计算预测的分类方法提供了可平行的迭代算法,并得出了抽样复杂性和趋同保证。与最新基准的全面数字比较表明,我们的方法在准确性-公平性交易曲线方面保持了竞争性业绩,同时在大型数据集上实现了优异的运行时间。我们还在多级、多交叉保护组和超过1M样本的开放数据集上评估了我们的方法。