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 nuanced policy objectives with the precise formalization requirements necessitated by ML models. In this paper, we aim to bridge the gap between ML model requirements 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, discussing 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：机器学习系统在公共部门中日益重要，应用于刑事司法、社会福利、金融欺诈检测和公共卫生等领域。尽管这些系统为机构决策过程带来巨大潜在益处（如提升效率与可靠性），但仍面临将细粒度政策目标与机器学习模型严苛的形式化要求相协调的挑战。本文旨在弥合机器学习模型需求与公共部门决策之间的鸿沟，系统梳理政策目标与机器学习模型之间常见脱节的关键技术挑战。我们聚焦于连接模型与操作环境的机器学习流水线关键环节，探讨代表性训练数据的重要性，并强调有利于有效决策的模型配置。此外，我们将这些挑战与新兴方法论进展（包括因果机器学习、领域自适应、不确定性量化及多目标优化）相关联，阐明协调机器学习与公共部门目标的未来路径。