Institute Homepage
Institute Homepage DE Sign In

Back

Social Foundations of Computation Conference Paper 2024

Allocation Requires Prediction Only if Inequality Is Low

Thumb ticker sm 20241104 hardt moritz 12 cleaned kleiner
Social Foundations of Computation
Moritz Hardt
  • Director

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

Author(s): Shirali, Ali and Abebe, Rediet* and Hardt, Moritz*
Book Title: Proceedings of the 41st International Conference on Machine Learning (ICML 2024)
Year: 2024
Month: July
Publisher: PMLR
Project(s):
Bibtex Type: Conference Paper (inproceedings)
Event Name: The Forty-First International Conference on Machine Learning (ICML)
State: Published
URL: https://proceedings.mlr.press/v235/shirali24a.html
Electronic Archiving: grant_archive
Note: *equal contribution
Links:

BibTex 

@inproceedings{shirali2024allocation,
  title = {Allocation Requires Prediction Only if Inequality Is Low},
  booktitle = {Proceedings of the 41st International Conference on Machine Learning (ICML 2024)},
  abstract = {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},
  publisher = {PMLR},
  month = jul,
  year = {2024},
  note = {*equal contribution},
  slug = {shirali2024allocation},
  author = {Shirali, Ali and Abebe, Rediet* and Hardt, Moritz*},
  url = {https://proceedings.mlr.press/v235/shirali24a.html},
  month_numeric = {7}
}