论文标题
对抗性使用阻尼和堆叠的学识渊博的表示
Adversarial Learned Fair Representations using Dampening and Stacking
论文作者
论文摘要
随着我们日常生活中的越来越多的决定变得自动化，需要提高机器学习算法的需求会增加。在公平表示学习中，我们的任务是找到对敏感变量进行审查的数据的适当表示。最近的工作旨在通过对抗性学习来学习公平的表现。本文通过引入一种新颖的算法来基于这项工作，该算法使用抑制和堆叠来学习对抗性公平表示。结果表明，我们的算法在审查和重建方面的早期工作都改善了。
As more decisions in our daily life become automated, the need to have machine learning algorithms that make fair decisions increases. In fair representation learning we are tasked with finding a suitable representation of the data in which a sensitive variable is censored. Recent work aims to learn fair representations through adversarial learning. This paper builds upon this work by introducing a novel algorithm which uses dampening and stacking to learn adversarial fair representations. Results show that that our algorithm improves upon earlier work in both censoring and reconstruction.
