Attribution-Scores and Causal Counterfactuals as Explanations in Artificial Intelligence
This is an incremental expository review that synthesizes existing approaches for researchers and practitioners interested in explainable AI.
The article discusses the importance of explanations in AI, particularly in explainable AI, by reviewing attribution-scores and causal counterfactuals as methods for providing insights, without presenting new experimental results or numerical findings.
In this expository article we highlight the relevance of explanations for artificial intelligence, in general, and for the newer developments in {\em 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 counterfactuals, and their use for score computation.