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

arXiv:2310.19091v2 Announce Type: replace
Abstract: 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 …

abstract applications arxiv benefits cs.cy cs.hc cs.lg decision detection efficiency financial financial fraud fraud fraud detection gap health justice machine machine learning making processes public public health public sector sector social stat.me systems toolkit type welfare while