mlsanityData to 7 Oct 2026

Attribution-Scores and Causal Counterfactuals as Explanations in Artificial Intelligence

arXiv 2303.02829 ↗First posted 6 Mar 2023cs.AI cs.DB cs.LGcited by 2

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Abstract (arXiv)

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.

Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Builds on 24 of its 66 references that are in the map. First posted on arXiv 6 Mar 2023.

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24 of 66 references.

References · 24

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12 more
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Citing papers per year: 2023: 2.

2 papers.

  1. 2023From Database Repairs to Causality in Databases and Beyondcited by 3
  2. 2023Attribution-Scores in Data Management and Explainable Machine Learningcited by 2

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