The Value of Prediction in Identifying the Worst-Off

Fischer-Abaigar, Unai; Kern, Christoph und Perdomo, Juan Carlos (2025): The Value of Prediction in Identifying the Worst-Off. ICML 2025: Forty-Second International Conference on Machine Learning, Vancouver, Canada, 13. - 19. Juli 2025. Singh, Aarti; Fazel, Maryam; Hsu, Daniel; Lacoste-Julien, Simon; Berkenkamp, Felix; Maharaj, Tegan; Wagstaff, Kiri und Zhu, Jerry (eds.) : Proceedings of Machine Learning Research. Vol. 267 MLResearchPress. pp. 17239-17261

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.

• Item Type: Conference or Workshop Item (Paper)
• Faculties: Mathematics, Computer Science and Statistics > Statistics > Chairs/Working Groups > Chair for Statistics and Data Science in Social Sciences and the Humanities
• Subjects: 300 Social sciences > 310 Statistics
• Language: English