Explainability Via Causal Self-Talk

• URL: http://arxiv.org/abs/2211.09937v1
• Date: Thu, 17 Nov 2022 23:17:01 GMT
• Title: Explainability Via Causal Self-Talk
• Authors: Nicholas A. Roy, Junkyung Kim, Neil Rabinowitz
• Abstract summary: Explaining the behavior of AI systems is an important problem that, in practice, is generally avoided. We describe an effective way to satisfy all the desiderata: train the AI system to build a causal model of itself. We implement this method in a simulated 3D environment, and show how it enables agents to generate faithful and semantically-meaningful explanations.
• Score: 9.149689942389923
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Explaining the behavior of AI systems is an important problem that, in practice, is generally avoided. While the XAI community has been developing an abundance of techniques, most incur a set of costs that the wider deep learning community has been unwilling to pay in most situations. We take a pragmatic view of the issue, and define a set of desiderata that capture both the ambitions of XAI and the practical constraints of deep learning. We describe an effective way to satisfy all the desiderata: train the AI system to build a causal model of itself. We develop an instance of this solution for Deep RL agents: Causal Self-Talk. CST operates by training the agent to communicate with itself across time. We implement this method in a simulated 3D environment, and show how it enables agents to generate faithful and semantically-meaningful explanations of their own behavior. Beyond explanations, we also demonstrate that these learned models provide new ways of building semantic control interfaces to AI systems.

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