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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 greatpotential benefits to institutional decision-making processes, such as improvedefficiency and reliability, they still face the challenge of aligning intricateand nuanced policy objectives with the precise formalization requirementsnecessitated by ML models. In this paper, we aim to bridge the gap between MLand public sector decision-making by presenting a comprehensive overview of keytechnical challenges where disjunctions between policy goals and ML modelscommonly arise. We concentrate on pivotal points of the ML pipeline thatconnect the model to its operational environment, delving into the significanceof representative training data and highlighting the importance of a modelsetup that facilitates effective decision-making. Additionally, we link thesechallenges with emerging methodological advancements, encompassing causal ML,domain adaptation, uncertainty quantification, and multi-objectiveoptimization, illustrating the path forward for harmonizing ML and publicsector objectives.

Bridging the Gap: Towards an Expanded Toolkit for ML-Supported Decision-Making in the Public Sector