[AISTATS 2025]This repository accompanies the paper To Give or Not to Give? The Impacts of Strategically Withheld Recourse -- Yatong Chen, Andrew Estornell, Yevgeniy Vorobeychik, Yang Liu.
Individuals often aim to reverse undesired outcomes in interactions with automated systems, like loan denials, by either implementing system-recommended actions (recourse), or manipulating their features. While providing recourse benefits users and enhances system utility, it also provides information about the decision process that can be used for more effective strategic manipulation, especially when the individuals collectively share such information with each other. We show that this tension leads rational utility-maximizing systems to frequently withhold recourse, resulting in decreased population utility, particularly impacting sensitive groups. To mitigate these effects, we explore the role of recourse subsidies, finding them effective in increasing the provision of recourse actions by rational systems, as well as lowering the potential social cost and mitigating unfairness caused by recourse withholding.
The result for synthetic experiments using simulated data is provided in the Jupyter notebook named 'strategically-withheld-recourse-AISTATS-submission.ipynb'. Detailed dscriptions of the data generating process can be found in Section 8 of the paper. Running the notebook will reproduce Figure 2 and 3 in the main paper.
If you want to cite our paper, please cite the following format:
@article{chen2025give,
title={To Give or Not to Give? The Impacts of Strategically Withheld Recourse},
author={Chen, Yatong and Estornell,Andrew and Vorobeychik,Yevgeniy and Liu, Yang},
booktitle={Artificial Intelligence and Statistics 2025},
organization={PMLR}
year={2025}
}