The Value of Prediction in Identifying the Worst-Off

Unai Fischer-Abaigar, Christoph Kern, Juan Carlos Perdomo
Proceedings of the 42nd International Conference on Machine Learning, PMLR 267:17239-17261, 2025.

Abstract

Machine learning is increasingly used in government programs to identify and support the most vulnerable individuals, prioritizing assistance for those at greatest risk over optimizing aggregate outcomes. This paper examines the welfare impacts of prediction in equity-driven contexts, and how they compare to other policy levers, such as expanding bureaucratic capacity. Through mathematical models and a real-world case study on long-term unemployment amongst German residents, we develop a comprehensive understanding of the relative effectiveness of prediction in surfacing the worst-off. Our findings provide clear analytical frameworks and practical, data-driven tools that empower policymakers to make principled decisions when designing these systems.

Cite this Paper

BibTeX
@InProceedings{pmlr-v267-fischer-abaigar25a, title = {The Value of Prediction in Identifying the Worst-Off}, author = {Fischer-Abaigar, Unai and Kern, Christoph and Perdomo, Juan Carlos}, booktitle = {Proceedings of the 42nd International Conference on Machine Learning}, pages = {17239--17261}, year = {2025}, editor = {Singh, Aarti and Fazel, Maryam and Hsu, Daniel and Lacoste-Julien, Simon and Berkenkamp, Felix and Maharaj, Tegan and Wagstaff, Kiri and Zhu, Jerry}, volume = {267}, series = {Proceedings of Machine Learning Research}, month = {13--19 Jul}, publisher = {PMLR}, pdf = {https://raw.githubusercontent.com/mlresearch/v267/main/assets/fischer-abaigar25a/fischer-abaigar25a.pdf}, url = {https://proceedings.mlr.press/v267/fischer-abaigar25a.html}, abstract = {Machine learning is increasingly used in government programs to identify and support the most vulnerable individuals, prioritizing assistance for those at greatest risk over optimizing aggregate outcomes. This paper examines the welfare impacts of prediction in equity-driven contexts, and how they compare to other policy levers, such as expanding bureaucratic capacity. Through mathematical models and a real-world case study on long-term unemployment amongst German residents, we develop a comprehensive understanding of the relative effectiveness of prediction in surfacing the worst-off. Our findings provide clear analytical frameworks and practical, data-driven tools that empower policymakers to make principled decisions when designing these systems.} }
Endnote
%0 Conference Paper %T The Value of Prediction in Identifying the Worst-Off %A Unai Fischer-Abaigar %A Christoph Kern %A Juan Carlos Perdomo %B Proceedings of the 42nd International Conference on Machine Learning %C Proceedings of Machine Learning Research %D 2025 %E Aarti Singh %E Maryam Fazel %E Daniel Hsu %E Simon Lacoste-Julien %E Felix Berkenkamp %E Tegan Maharaj %E Kiri Wagstaff %E Jerry Zhu %F pmlr-v267-fischer-abaigar25a %I PMLR %P 17239--17261 %U https://proceedings.mlr.press/v267/fischer-abaigar25a.html %V 267 %X Machine learning is increasingly used in government programs to identify and support the most vulnerable individuals, prioritizing assistance for those at greatest risk over optimizing aggregate outcomes. This paper examines the welfare impacts of prediction in equity-driven contexts, and how they compare to other policy levers, such as expanding bureaucratic capacity. Through mathematical models and a real-world case study on long-term unemployment amongst German residents, we develop a comprehensive understanding of the relative effectiveness of prediction in surfacing the worst-off. Our findings provide clear analytical frameworks and practical, data-driven tools that empower policymakers to make principled decisions when designing these systems.
APA
Fischer-Abaigar, U., Kern, C. & Perdomo, J.C.. (2025). The Value of Prediction in Identifying the Worst-Off. Proceedings of the 42nd International Conference on Machine Learning, in Proceedings of Machine Learning Research 267:17239-17261 Available from https://proceedings.mlr.press/v267/fischer-abaigar25a.html.

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