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September 20, 2025

Explain It as Simple as Possible, but No Simpler – Explanation via Model Simplification for Addressing Inferential Gap (Abstract Reprint)

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A general framework improves explanation generation for AI systems by addressing inferential capability gaps.
The model uses transformations to create simplified representations, enhancing understanding of complex decisions.
Experiments and user studies evaluate the framework's effectiveness, focusing on stochastic planning scenarios.
Existing explanation methods can be viewed as instances of this broader transformation-based approach.

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This framework demonstrates how model simplification enhances explanation in AI systems, suggesting potential applications in stochastic planning.

Sreedharan et al. (2025) studied this question.

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This framework demonstrates how model simplification enhances explanation in AI systems, suggesting potential applications in stochastic planning.