标题： 不要扔掉它！ 在公平决策中的无标签数据的效用 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表现出各种偏见的数据上进行训练的。 决策者通常旨在根据某种假设或预期为无偏的真值目标做出决策，即在社会显著群体中均匀分布。 在许多实际情况下，真值无法直接观察到，而是必须依赖于真值的有偏代理度量，即数据中的有偏标签。 此外，数据通常是选择性标注的，即即使是有偏的标签也只在一小部分获得积极决策的数据中被观察到。 为了克服标签和选择偏差，最近的工作提出通过i) 在每个时间步在线训练新策略以及ii) 将公平性作为性能约束来学习随机探索决策策略。 然而，现有方法仅使用已标记的数据，忽视了大量未标记的数据，因此在不同时间点学习的决策策略中表现出高不稳定性和方差。 在本文中，我们提出了一种基于变分自编码器的实用公平决策的新方法。 我们的方法利用已标记和未标记数据学习一个无偏的数据表示，并在在线过程中使用这些表示来学习一个策略。 使用合成数据，我们实证验证了我们的方法能够根据真值以低方差收敛到最优（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学过程，而且产生的策略在公平性和效用方面都优于之前的方法。 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.