Machine learning predictors are successfully deployed in applications ranging from disease diagnosis, to predicting credit scores, to image recognition. Even when the overall accuracy is high, the predictions often have systematic biases that harm specific subgroups, especially for subgroups that are minorities in the training data. We develop a rigorous framework of multiaccuracy auditing and post-processing to improve predictor accuracies across identifiable subgroups. Our algorithm, MultiaccuracyBoost, works in any setting where we have black-box access to a predictor and a relatively small set of labeled data for auditing. We prove guarantees on the convergence rate of the algorithm and show that it improves overall accuracy at each step. Importantly, if the initial model is accurate on an identifiable subgroup, then the post-processed model will be also. We demonstrate the effectiveness of this approach on diverse applications in image classification, finance, and population health. MultiaccuracyBoost can improve subpopulation accuracy (e.g. for `black women') even when the sensitive features (e.g. `race', `gender') are not known to the algorithm.

使用MULTIACCURACY-BOOST算法，通过多准确度审计和后处理，从可识别的子组中提高模型的准确性，提高预测公正性和可追溯性，能在黑盒模型情况下改善少数族裔在图像识别、金融和人口健康应用中的准确性。

Multiaccuracy: 在分类中进行黑盒后处理以实现公平