  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.
