@inproceedings{
chang2024whos,
title={Who{\textquoteright}s Gaming the System? A Causally-Motivated Approach for Detecting Strategic Adaptation},
author={Trenton Chang and Lindsay Warrenburg and Sae-Hwan Park and Ravi B Parikh and Maggie Makar and Jenna Wiens},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=PXGY9Fz8vC}
}
In many settings, machine learning models may be used to inform decisions that impact individuals or entities who interact with the model. Such entities, or *agents,* may *game* model decisions by manipulating their inputs to the model to obtain better outcomes and maximize some utility. We consider a multi-agent setting where the goal is to identify the “worst offenders:” agents that are gaming most aggressively. However, identifying such agents is difficult without knowledge of their utility function. Thus, we introduce a framework in which each agent’s tendency to game is parameterized via a scalar. We show that this gaming parameter is only partially identifiable. By recasting the problem as a causal effect estimation problem where different agents represent different “treatments,” we prove that a ranking of all agents by their gaming parameters is identifiable. We present empirical results in a synthetic data study validating the usage of causal effect estimation for gaming detection and show in a case study of diagnosis coding behavior in the U.S. that our approach highlights features associated with gaming.
Who’s Gaming the System? A Causally-Motivated Approach for Detecting Strategic Adaptation
Trenton Chang Lindsay Warrenburg Sae-Hwan Park Ravi B Parikh Maggie Makar Jenna Wiens