Causal Analysis of Agent Behavior for AI Safety

• URL: http://arxiv.org/abs/2103.03938v1
• Date: Fri, 5 Mar 2021 20:51:12 GMT
• Title: Causal Analysis of Agent Behavior for AI Safety
• Authors: Gr\'egoire D\'eletang, Jordi Grau-Moya, Miljan Martic, Tim Genewein, Tom McGrath, Vladimir Mikulik, Markus Kunesch, Shane Legg, Pedro A. Ortega
• Abstract summary: We show a methodology for investigating the causal mechanisms that drive the behaviour of artificial agents. Six use cases are covered, each addressing a typical question an analyst might ask about an agent.
• Score: 16.764915383473326
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: As machine learning systems become more powerful they also become increasingly unpredictable and opaque. Yet, finding human-understandable explanations of how they work is essential for their safe deployment. This technical report illustrates a methodology for investigating the causal mechanisms that drive the behaviour of artificial agents. Six use cases are covered, each addressing a typical question an analyst might ask about an agent. In particular, we show that each question cannot be addressed by pure observation alone, but instead requires conducting experiments with systematically chosen manipulations so as to generate the correct causal evidence.

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