Summary of Who’s Gaming the System? a Causally-motivated Approach For Detecting Strategic Adaptation, by Trenton Chang et al.
Who’s Gaming the System? A Causally-Motivated Approach for Detecting Strategic Adaptation
by Trenton Chang, Lindsay Warrenburg, Sae-Hwan Park, Ravi B. Parikh, Maggie Makar, Jenna Wiens
First submitted to arxiv on: 2 Dec 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper proposes a framework to detect and rank agents that are most aggressively gaming machine learning models in a multi-agent setting. The framework parameterizes each agent’s tendency to game via a scalar, which is only partially identifiable without knowledge of their utility function. By recasting the problem as a causal effect estimation problem, the authors prove that a ranking of all agents by their gaming parameters is identifiable. Empirical results from synthetic data and a real-world case study demonstrate the effectiveness of this approach in detecting gaming behavior. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps us figure out which people or groups are trying to cheat on computer systems that make decisions based on what they do. It’s hard to catch cheaters because we don’t know their goals, but by treating each person as if they were getting a different kind of treatment, we can still identify the worst offenders. The authors show that this approach works with fake data and even in real-life situations, like doctors coding patient symptoms. |
Keywords
» Artificial intelligence » Machine learning » Synthetic data
