Selective labels occur when label observations are subject to a
decision-making process; e.g., diagnoses that depend on the administration of
laboratory tests. We study a clinically-inspired selective label problem called
disparate censorship, where labeling biases vary across subgroups and unlabeled
individuals are imputed as "negative" (i.e., no diagnostic test = no illness).
Machine learning models naively trained on such labels could amplify labeling
bias. Inspired by causal models of selective labels, we propose Disparate
Censorship Expectation-Maximization (DCEM), an algorithm for learning in the
presence of disparate censorship. We theoretically analyze how DCEM mitigates
the effects of disparate censorship on model performance. We validate DCEM on
synthetic data, showing that it improves bias mitigation (area between ROC
curves) without sacrificing discriminative performance (AUC) compared to
baselines. We achieve similar results in a sepsis classification task using
clinical data.

我们提出了一种名为不平等审查的算法，用于处理标签的选择性问题，并通过理论分析和验证实验证明了其在模型性能上的好处。

从有偏选择标签到伪标签：一种期望最大化学习框架应对有偏决策

From Biased Selective Labels to Pseudo-Labels: An  Expectation-Maximization Framework for Learning from Biased Decisions

We explore the problem of learning under selective labels in the context of
algorithm-assisted decision making. Selective labels is a pervasive selection
bias problem that arises when historical decision making blinds us to the true
outcome for certain instances. Examples of this are common in many
applications, ranging from predicting recidivism using pre-trial release data
to diagnosing patients. In this paper we discuss why selective labels often
cannot be effectively tackled by standard methods for adjusting for sample
selection bias, even if there are no unobservables. We propose a data
augmentation approach that can be used to either leverage expert consistency to
mitigate the partial blindness that results from selective labels, or to
empirically validate whether learning under such framework may lead to
unreliable models prone to systemic discrimination.

本论文探讨选择性标签下算法辅助决策学习的问题，介绍为什么标准方法无法有效处理选择性标签的普遍选择偏误问题，提出了一种数据增强方法，既可以利用专家一致性减轻选择性标签导致的部分盲目性，也可以经验验证学习是否会导致易受系统性歧视的不可靠模型。