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Attribution-Scores and Causal Counterfactuals as Explanations in Artificial Intelligence

Leopoldo Bertossi

Teacher at SKEMA Canada

In this expository article we highlight the relevance of expla- nations for artificial intelligence, in general, and for the newer devel- opments in explainable AI, referring to origins and connections of and among different approaches. We describe in simple terms, explanations in data management and machine learning that are based on attribution- scores, and counterfactuals as found in the area of causality. We elaborate on the importance of logical reasoning when dealing with counterfactu- als, and their use for score computation.


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