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Incentive Mechanisms for Strategic Classification and Regression Problems

Published: 13 July 2022 Publication History

Abstract

We study the design of a class of incentive mechanisms that can effectively prevent cheating in a strategic classification and regression problem. A conventional strategic classification or regression problem is modeled as a Stackelberg game, or a principal-agent problem between the designer of a classifier (the principal) and individuals subject to the classifier's decisions (the agents), potentially from different demographic groups. The former benefits from the accuracy of its decisions, whereas the latter may have an incentive to game the algorithm into making favorable but erroneous decisions. While prior works tend to focus on how to design an algorithm to be more robust to such strategic maneuvering, this study focuses on an alternative, which is to design incentive mechanisms to shape the utilities of the agents and induce effort that genuinely improves their skills, which in turn benefits both parties in the Stackelberg game. Specifically, the principal and the mechanism provider (which could also be the principal itself) move together in the first stage, publishing and committing to a classifier and an incentive mechanism. The agents are (simultaneous) second movers and best respond to the published classifier and incentive mechanism. When an agent's strategic action merely changes its observable features, it hurts the performance of the algorithm. However, if the action leads to improvement in the agent's true label, it not only helps the agent achieve better decision outcomes, but also preserves the performance of the algorithm. We study how a subsidy mechanism can induce improvement actions, positively impact a number of social well-being metrics, such as the overall skill levels of the agents (efficiency) and positive or true positive rate differences between different demographic groups (fairness).

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Cited By

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  • (2024)Performative federated learningProceedings of the Thirty-Eighth AAAI Conference on Artificial Intelligence and Thirty-Sixth Conference on Innovative Applications of Artificial Intelligence and Fourteenth Symposium on Educational Advances in Artificial Intelligence10.1609/aaai.v38i11.29191(12938-12946)Online publication date: 20-Feb-2024
  • (2023)Collaboration as a Mechanism for More Robust Strategic Classification2023 62nd IEEE Conference on Decision and Control (CDC)10.1109/CDC49753.2023.10383651(235-240)Online publication date: 13-Dec-2023

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cover image ACM Conferences
EC '22: Proceedings of the 23rd ACM Conference on Economics and Computation
July 2022
1269 pages
ISBN:9781450391504
DOI:10.1145/3490486
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Published: 13 July 2022

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  1. strategic classification
  2. strategic regression
  3. subsidy mechanisms

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View all
  • (2024)Performative federated learningProceedings of the Thirty-Eighth AAAI Conference on Artificial Intelligence and Thirty-Sixth Conference on Innovative Applications of Artificial Intelligence and Fourteenth Symposium on Educational Advances in Artificial Intelligence10.1609/aaai.v38i11.29191(12938-12946)Online publication date: 20-Feb-2024
  • (2023)Collaboration as a Mechanism for More Robust Strategic Classification2023 62nd IEEE Conference on Decision and Control (CDC)10.1109/CDC49753.2023.10383651(235-240)Online publication date: 13-Dec-2023

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