In response to a national and international awakening on the issues of
anti-Blackness and systemic discrimination, we have penned this piece to serve
as a resource for allies in the AI community who are wondering how they can
more effectively engage with dismantling racist systems. This work aims to help
elucidate areas where the AI community actively and passively contributes to
anti-Blackness and offers actionable items on ways to reduce harm.

为 AI 界的盟友们提供资源，帮助他们理解 AI 界积极和消极参与反黑人主义的方面，并提供可行的措施以减少伤害。

AI 社区中对抗反黑色主义

Combating Anti-Blackness in the AI Community

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。