The Value of Prediction in Identifying the Worst-Off
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
Text
Fischer-Abaigar et al. "The Value of Prediction in Identifying the Worst-Off." Proceedings of the 42nd International Conference on Machine Learning, 2025.Markdown
[Fischer-Abaigar et al. "The Value of Prediction in Identifying the Worst-Off." Proceedings of the 42nd International Conference on Machine Learning, 2025.](https://mlanthology.org/icml/2025/fischerabaigar2025icml-value/)BibTeX
@inproceedings{fischerabaigar2025icml-value,
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},
year = {2025},
pages = {17239-17261},
volume = {267},
url = {https://mlanthology.org/icml/2025/fischerabaigar2025icml-value/}
}