The Value of Prediction in Identifying the Worst-Off
Fuente:
arXiv
Gespeichert in:
| Hauptverfasser: | , , |
|---|---|
| Format: | Preprint |
| Veröffentlicht: |
2025
|
| Schlagworte: | |
| Online-Zugang: | |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| _version_ | 1866908446323900416 |
|---|---|
| author | Fischer-Abaigar, Unai Kern, Christoph Perdomo, Juan Carlos |
| author_facet | Fischer-Abaigar, Unai Kern, Christoph Perdomo, Juan Carlos |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2501_19334 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | The Value of Prediction in Identifying the Worst-Off Fischer-Abaigar, Unai Kern, Christoph Perdomo, Juan Carlos Computers and Society Machine Learning 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. |
| title | The Value of Prediction in Identifying the Worst-Off |
| topic | Computers and Society Machine Learning |
| url | https://arxiv.org/abs/2501.19334 |