Explainable AI

Current research on explainability and interpretability of machine learning algorithms

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Causes and Explanations: A Structural-Model Approach. Part II: Explanations

Joseph Y. Halpern, Judea Pearl. 2002

[ArXiv]    

We propose new definitions of (causal) explanation, using structural equations to model counterfactuals. The definition is based on the notion of actual cause, as defined and motivated in a companion paper. Essentially, an explanation is a fact that is not known for certain but, if found to be true, would constitute an actual cause of the fact to be explained, regardless of the agent’s initial uncertainty. We show that the definition handles well a number of problematic examples from the literature.