Summary of Regulation Games For Trustworthy Machine Learning, by Mohammad Yaghini et al.
Regulation Games for Trustworthy Machine Learning
by Mohammad Yaghini, Patty Liu, Franziska Boenisch, Nicolas Papernot
First submitted to arxiv on: 5 Feb 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Computer Science and Game Theory (cs.GT); Machine Learning (stat.ML)
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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 In this research paper, the authors propose a framework for trustworthy machine learning (ML) that considers multiple aspects of trust, including fairness and privacy. They view trustworthy ML as a multi-objective multi-agent optimization problem, which is naturally formulated as a game-theoretic problem they call regulation games. The authors illustrate their approach with a specific game instance, the SpecGame, where an ML model builder interacts with regulators who design penalties to enforce compliance with their specifications while encouraging participation. To find socially optimal solutions, they introduce ParetoPlay, a novel equilibrium search algorithm that ensures agents remain on the Pareto frontier of their objectives and avoids inefficient equilibria. The authors demonstrate the effectiveness of their approach by simulating the SpecGame through ParetoPlay for a gender classification application, showing that regulators can enforce a differential privacy budget that is 4.0 lower on average if they specify their desired guarantee first. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper creates a new framework for trustworthy machine learning that combines fairness and privacy. Instead of just focusing on one aspect, this approach looks at all the different ways to make sure AI models are trustworthy. It uses game theory to solve problems and makes sure everyone is working together fairly. The authors show how this works by playing out a scenario where an AI model builder has to work with regulators who want to make sure the model is fair and private. |
Keywords
* Artificial intelligence * Classification * Machine learning * Optimization
