标题： 不要扔掉它！ 无标签数据在公平决策中的作用 显示英文标题

标题： Don't Throw it Away! The Utility of Unlabeled Data in Fair Decision Making

摘要： 决策算法在实践中通常是在包含各种偏见的数据上进行训练的。 决策者常常旨在基于某些被认为是无偏见的、即在社会显著群体中均匀分布的地面真实目标做出决策。 在许多实际场景中，地面真实目标无法直接观察到，相反，我们不得不依赖于地面真实目标的有偏见代理度量，即数据中的有偏见标签。 此外，数据往往是有选择性标记的，即即使是有偏见的标签，也只对收到积极决策的小部分数据进行观察。 为了克服标签和选择偏见，近期的工作提出通过以下两种方式学习随机探索决策策略：i）在每个时间步长上在线训练新策略；ii）将公平性作为性能的约束条件。 然而，现有方法仅使用标记数据，忽略了大量未标记数据，因此在不同时间学到的决策策略存在高不稳定性及方差问题。 本文提出了一种基于变分自编码器的实用公平决策制定新方法。 我们的方法利用标记和未标记数据学习无偏的数据表示，并使用这些表示在在线过程中学习策略。 通过合成数据，我们实证验证了我们的方法能够以低方差收敛至根据地面真实目标得出的最佳（公平）策略。 在现实世界实验中，我们进一步表明，我们的训练方法不仅提供了更稳定的训练过程，而且生成的策略在公平性和实用性方面都优于先前的方法。 显示英文摘要

摘要： Decision making algorithms, in practice, are often trained on data that exhibits a variety of biases. Decision-makers often aim to take decisions based on some ground-truth target that is assumed or expected to be unbiased, i.e., equally distributed across socially salient groups. In many practical settings, the ground-truth cannot be directly observed, and instead, we have to rely on a biased proxy measure of the ground-truth, i.e., biased labels, in the data. In addition, data is often selectively labeled, i.e., even the biased labels are only observed for a small fraction of the data that received a positive decision. To overcome label and selection biases, recent work proposes to learn stochastic, exploring decision policies via i) online training of new policies at each time-step and ii) enforcing fairness as a constraint on performance. However, the existing approach uses only labeled data, disregarding a large amount of unlabeled data, and thereby suffers from high instability and variance in the learned decision policies at different times. In this paper, we propose a novel method based on a variational autoencoder for practical fair decision-making. Our method learns an unbiased data representation leveraging both labeled and unlabeled data and uses the representations to learn a policy in an online process. Using synthetic data, we empirically validate that our method converges to the optimal (fair) policy according to the ground-truth with low variance. In real-world experiments, we further show that our training approach not only offers a more stable learning process but also yields policies with higher fairness as well as utility than previous approaches.