Position: Scarce Resource Allocations That Rely on Machine Learning Should Be Randomized
Abstract
Contrary to traditional deterministic notions of algorithmic fairness, this paper argues that fairly allocating scarce resources using machine learning often requires randomness. We address why, when, and how to randomize by offering a set of stochastic procedures that more adequately account for all of the claims individuals have to allocations of social goods or opportunities and effectively balances their interests.
Cite
Text
Jain et al. "Position: Scarce Resource Allocations That Rely on Machine Learning Should Be Randomized." International Conference on Machine Learning, 2024.Markdown
[Jain et al. "Position: Scarce Resource Allocations That Rely on Machine Learning Should Be Randomized." International Conference on Machine Learning, 2024.](https://mlanthology.org/icml/2024/jain2024icml-position/)BibTeX
@inproceedings{jain2024icml-position,
title = {{Position: Scarce Resource Allocations That Rely on Machine Learning Should Be Randomized}},
author = {Jain, Shomik and Creel, Kathleen and Wilson, Ashia Camage},
booktitle = {International Conference on Machine Learning},
year = {2024},
pages = {21148-21169},
volume = {235},
url = {https://mlanthology.org/icml/2024/jain2024icml-position/}
}