Faithful, Interpretable Model Explanations via Causal Abstraction | SAIL Blog

Seeking human-intelligible explanations

Seeking human-intelligible explanations Explaining why a deep learning model makes the predictions it does has emerged as one of the most challenging questions in AI ( Lipton 2018 , Pearl 2019 ). There is something of a paradox about this, however. After all, deep learning models are closed, deterministic systems that give us ground-truth knowledge of the causal relationships between all their components. Thus, their behavior is in many ways easy to explain: one can mechanistically walk through the mathematical operations or the associated computer code, and this can be done at varying levels

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• Timothy Kostolansky
• Wendy Sun
• Ishaan Panigrahi

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