We introduce a method, MMD-B-Fair, to learn fair representations of data via kernel two-sample testing. We find neural features of our data where a maximum mean discrepancy (MMD) test cannot distinguish between different values of sensitive attributes, while preserving information about the target. Minimizing the power of an MMD test is more difficult than maximizing it (as done in previous work), because the test threshold's complex behavior cannot be simply ignored. Our method exploits the simple asymptotics of block testing schemes to efficiently find fair representations without requiring the complex adversarial optimization or generative modelling schemes widely used by existing work on fair representation learning. We evaluate our approach on various datasets, showing its ability to "hide" information about sensitive attributes, and its effectiveness in downstream transfer tasks.

翻译：我们引入了一种方法,即MMD-B-Fair,通过两样样的内核测试来学习公平的数据表述。我们发现我们的数据有神经特征,在其中,最大平均差异(MMD)测试无法区分敏感属性的不同值,同时保存关于目标的信息。 最大限度地减少MMD测试的力量比(像以前的工作所做的那样)尽可能扩大它要困难得多,因为测试门槛的复杂行为不能被完全忽视。 我们的方法利用区块测试计划的简单空洞来有效找到公平代表,而不需要现有的公平代表性学习工作所广泛使用的复杂的对抗性优化或基因化建模计划。 我们评估了我们对各种数据集的做法,显示了它“隐藏”敏感属性信息的能力及其在下游转移任务中的有效性。