Incentivizing Recourse through Auditing in Strategic Classification
Andrew Estornell, Yatong Chen, Sanmay Das, Yang Liu, Yevgeniy Vorobeychik
Abstract
The increasing automation of high-stakes decisions with direct impact on the lives and well-being of individuals raises a number of important considerations. Prominent among these is strategic behavior by individuals hoping to achieve a more desirable outcome. Two forms of such behavior are commonly studied: 1) misreporting of individual attributes, and 2) recourse, or actions that truly change such attributes. The former involves deception, and is inherently undesirable, whereas the latter may well be a desirable goal insofar as it changes true individual qualification. We study misreporting and recourse as strategic choices by individuals within a unified framework. In particular, we propose auditing as a means to incentivize recourse actions over attribute manipulation, and characterize optimal audit policies for two types of principals, utility-maximizing and recourse-maximizing. Additionally, we consider subsidies as an incentive for recourse over manipulation, and show that even a utility-maximizing principal would be willing to devote a considerable amount of audit budget to providing such subsidies. Finally, we consider the problem of optimizing fines for failed audits, and bound the total cost incurred by the population as a result of audits.
BibTeX
@inproceedings{ijcai2023p45,
title = {Incentivizing Recourse through Auditing in Strategic Classification},
author = {Estornell, Andrew and Chen, Yatong and Das, Sanmay and Liu, Yang and Vorobeychik, Yevgeniy},
booktitle = {Proceedings of the Thirty-Second International Joint Conference on
Artificial Intelligence, {IJCAI-23}},
publisher = {International Joint Conferences on Artificial Intelligence Organization},
editor = {Edith Elkind},
pages = {400--408},
year = {2023},
month = {8},
note = {Main Track},
doi = {10.24963/ijcai.2023/45},
url = {https://doi.org/10.24963/ijcai.2023/45},
}