论文标题
公平的NLP模型，带有差异私人文本编码器
Fair NLP Models with Differentially Private Text Encoders
论文作者
论文摘要
编码的文本表示通常会捕获有关个人（例如种族或性别）的敏感属性，这些属性引起了隐私问题，并且可能使某些群体不公平的下游模型。在这项工作中，我们提出了联邦制，这种方法结合了差异隐私和对抗性培训的想法，以学习私人文本表示形式，这也可以诱导更公平的模型。我们从经验上评估了三个NLP数据集上的表示形式的隐私与下游模型的公平性和准确性之间的权衡。我们的结果表明，联邦对以前的方法持续改善，因此表明隐私和公平可以互相加强。
Encoded text representations often capture sensitive attributes about individuals (e.g., race or gender), which raise privacy concerns and can make downstream models unfair to certain groups. In this work, we propose FEDERATE, an approach that combines ideas from differential privacy and adversarial training to learn private text representations which also induces fairer models. We empirically evaluate the trade-off between the privacy of the representations and the fairness and accuracy of the downstream model on four NLP datasets. Our results show that FEDERATE consistently improves upon previous methods, and thus suggest that privacy and fairness can positively reinforce each other.
