The distribution of crucial medical goods and services in conditions of scarcity is among the most important, albeit contested, areas of public policy development. Policymakers must strike a balance between multiple efficiency and fairness objectives, while reconciling disparate value judgments from a diverse set of stakeholders. We present a general framework for combining ethical theory, data modeling, and stakeholder input in this process and illustrate through a case study on designing organ transplant allocation policies. We develop a novel analytical tool, based on machine learning and optimization, designed to facilitate efficient and wide-ranging exploration of policy outcomes across multiple objectives. Such a tool enables all stakeholders, regardless of their technical expertise, to more effectively engage in the policymaking process by developing evidence-based value judgments based on relevant tradeoffs.

中文翻译：

---

Ethics-by-design：使用机器学习进行高效、公平和包容的资源分配。

在稀缺条件下分配关键医疗产品和服务是公共政策发展中最重要的领域之一，尽管存在争议。政策制定者必须在多重效率和公平目标之间取得平衡，同时协调来自不同利益相关者的不同价值判断。我们提出了一个在这个过程中结合伦理理论、数据建模和利益相关者输入的一般框架，并通过一个关于设计器官移植分配政策的案例研究来说明。我们开发了一种基于机器学习和优化的新型分析工具，旨在促进跨多个目标的政策结果的有效和广泛探索。这样的工具使所有利益相关者，无论他们的技术专长如何，