Summary of Games For Ai Control: Models Of Safety Evaluations Of Ai Deployment Protocols, by Charlie Griffin et al.
Games for AI Control: Models of Safety Evaluations of AI Deployment Protocols
by Charlie Griffin, Louis Thomson, Buck Shlegeris, Alessandro Abate
First submitted to arxiv on: 12 Sep 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Machine Learning (cs.LG)
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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 AI Control, a red-teaming exercise is used to evaluate the safety and usefulness of deployment protocols for untrusted AIs. This paper introduces AI-Control Games, a formal decision-making model that represents this exercise as a multi-objective, partially observable, stochastic game. The authors also develop methods for finding optimal protocols in AI-Control Games by reducing them to a set of zero-sum partially observable stochastic games. The paper applies this formalism to model, evaluate, and synthesise protocols for deploying untrusted language models as programming assistants, with a focus on Trusted Monitoring protocols that use weaker language models and limited human assistance. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary AI researchers are trying to figure out how to safely use artificial intelligence (AI) in real-life situations. They want to make sure AI doesn’t get too powerful or do bad things. To do this, they’re using a special game-playing exercise called red-teaming. In this exercise, one person tries to design an AI protocol and another person tries to break it. This paper creates a new way to model this game as a math problem. It also shows how to solve the math problem to find the best AI protocols. The authors use this new method to study how to safely use language models (which are like super-smart computer programs) to help humans with programming tasks. |
