تقرير
Allocation Requires Prediction Only if Inequality Is Low
العنوان: | Allocation Requires Prediction Only if Inequality Is Low |
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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. |