论文标题
黑盒预测的公平包装
Fair Wrapping for Black-box Predictions
论文作者
论文摘要
我们向后处理（“包装”）介绍了一个新的技术系列，以减少其偏见。我们的技术建立在最近对不当损失函数的分析的基础上，其优化可以纠正预测的任何扭曲，不公平被视为扭曲。在后处理中，我们学习了一个包装函数，我们将其定义为$α$ -tree，可修改预测。我们提供两种通用的增强算法，以学习$α$树。我们表明，我们的修改在$α$树的组成，概括，可解释性和KL差异方面具有吸引人的属性。我们以三个公平的概念来体现我们对技术的使用：有条件的价值，机会平等和统计平等；并在几个随时可用的数据集上提供实验。
We introduce a new family of techniques to post-process ("wrap") a black-box classifier in order to reduce its bias. Our technique builds on the recent analysis of improper loss functions whose optimization can correct any twist in prediction, unfairness being treated as a twist. In the post-processing, we learn a wrapper function which we define as an $α$-tree, which modifies the prediction. We provide two generic boosting algorithms to learn $α$-trees. We show that our modification has appealing properties in terms of composition of $α$-trees, generalization, interpretability, and KL divergence between modified and original predictions. We exemplify the use of our technique in three fairness notions: conditional value-at-risk, equality of opportunity, and statistical parity; and provide experiments on several readily available datasets.
