Transparency of algorithmic systems entails exposing system properties to various stakeholders for purposes that include understanding, improving, and/or contesting predictions. The machine learning (ML) community has mostly considered explainability as a proxy for transparency. With this work, we seek to encourage researchers to study uncertainty as a form of transparency and practitioners to communicate uncertainty estimates to stakeholders. First, we discuss methods for assessing uncertainty. Then, we describe the utility of uncertainty for mitigating model unfairness, augmenting decision-making, and building trustworthy systems. We also review methods for displaying uncertainty to stakeholders and discuss how to collect information required for incorporating uncertainty into existing ML pipelines. Our contribution is an interdisciplinary review to inform how to measure, communicate, and use uncertainty as a form of transparency.

中文翻译：

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作为透明度的一种形式的不确定性：测量、交流和使用不确定性

算法系统的透明度需要向各种利益相关者公开系统属性，目的包括理解、改进和/或竞争预测。机器学习 (ML) 社区主要将可解释性视为透明度的代表。通过这项工作，我们寻求鼓励研究人员将不确定性作为一种透明度形式进行研究，并鼓励从业者向利益相关者传达不确定性估计。首先，我们讨论评估不确定性的方法。然后，我们描述了不确定性在减轻模型不公平性、增强决策和构建可信赖系统方面的效用。我们还审查了向利益相关者展示不确定性的方法，并讨论了如何收集将不确定性纳入现有 ML 管道所需的信息。