We argue that insurance can act as an analogon for the social situatedness of machine learning systems, hence allowing machine learning scholars to take insights from the rich and interdisciplinary insurance literature. Tracing the interaction of uncertainty, fairness and responsibility in insurance provides a fresh perspective on fairness in machine learning. We link insurance fairness conceptions to their machine learning relatives, and use this bridge to problematize fairness as calibration. In this process, we bring to the forefront two themes that have been largely overlooked in the machine learning literature: responsibility and aggregate-individual tensions.

翻译：本文认为，保险可作为机器学习系统社会嵌入性的类比物，从而使机器学习研究者能够借鉴丰富且跨学科的保险文献中的见解。通过追溯保险领域中不确定性、公平性与责任三者的相互作用，可以为机器学习中的公平性研究提供全新视角。我们将保险公平性概念与机器学习中的相关概念进行关联，并借助这一桥梁对"将公平性等同于校准"的观点提出质疑。在此过程中，我们揭示了机器学习文献中普遍忽视的两大主题：责任问题与集体-个体张力。