تقرير
Allocation Requires Prediction Only if Inequality Is Low
| العنوان: | Allocation Requires Prediction Only if Inequality Is Low |
|---|---|
| المؤلفون: | Shirali, Ali, Abebe, Rediet, Hardt, Moritz |
| سنة النشر: | 2024 |
| المجموعة: | Computer Science |
| مصطلحات موضوعية: | Computer Science - Machine Learning, Computer Science - Computers and Society, Economics - Theoretical Economics |
| الوصف: | Algorithmic predictions are emerging as a promising solution concept for efficiently allocating societal resources. Fueling their use is an underlying assumption that such systems are necessary to identify individuals for interventions. We propose a principled framework for assessing this assumption: Using a simple mathematical model, we evaluate the efficacy of prediction-based allocations in settings where individuals belong to larger units such as hospitals, neighborhoods, or schools. We find that prediction-based allocations outperform baseline methods using aggregate unit-level statistics only when between-unit inequality is low and the intervention budget is high. Our results hold for a wide range of settings for the price of prediction, treatment effect heterogeneity, and unit-level statistics' learnability. Combined, we highlight the potential limits to improving the efficacy of interventions through prediction. Comment: Appeared in Forty-first International Conference on Machine Learning (ICML), 2024 |
| نوع الوثيقة: | Working Paper |
| URL الوصول: | http://arxiv.org/abs/2406.13882 |
| رقم الأكسشن: | edsarx.2406.13882 |
| قاعدة البيانات: | arXiv |
| الوصف غير متاح. |