Bridging the Gap: Towards an Expanded Toolkit for ML-Supported
  Decision-Making in the Public Sector
Unai Fischer Abaigar Christoph Kern Noam Barda Frauke Kreuter
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.