Machine Learning (ML) systems are becoming instrumental in the public sector, with applications spanning areas like criminal justice, social welfare, financial fraud detection, and public health. While these systems offer great potential benefits to institutional decision-making processes, such as improved efficiency and reliability, they still face the challenge of aligning intricate and nuanced policy objectives with the precise formalization requirements necessitated by ML models. In this paper, we aim to bridge the gap between ML and public sector decision-making by presenting a comprehensive overview of key technical challenges where disjunctions between policy goals and ML models commonly arise. We concentrate on pivotal points of the ML pipeline that connect the model to its operational environment, delving into the significance of representative training data and highlighting the importance of a model setup that facilitates effective decision-making. Additionally, we link these challenges with emerging methodological advancements, encompassing causal ML, domain adaptation, uncertainty quantification, and multi-objective optimization, illustrating the path forward for harmonizing ML and public sector objectives.

翻译：机器学习（ML）系统正逐步在公共领域发挥关键作用，其应用涵盖刑事司法、社会福利、金融欺诈检测及公共卫生等多个方面。尽管这些系统为机构决策流程带来了巨大的潜在效益（如提升效率与可靠性），但它们仍面临一项核心挑战：如何将复杂精密的政策目标与ML模型所需的精确形式化要求相统一。本文旨在通过系统梳理政策目标与ML模型之间普遍存在脱节的关键技术难点，弥合二者在公共部门决策中的鸿沟。我们聚焦于连接模型与运行环境的ML流水线关键环节，深入探讨代表性训练数据的重要性，并强调有助于实现有效决策的模型配置原则。同时，我们将这些挑战与新兴方法论进展（涵盖因果机器学习、领域自适应、不确定性量化及多目标优化）相衔接，勾勒出推进ML与公共部门目标协同发展的演进路径。