Explainable AI

Current research on explainability and interpretability of machine learning algorithms

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Defining Explanation in Probabilistic Systems

Urszula Chajewska, Joseph Y. Halpern. 2013

[ArXiv]    

As probabilistic systems gain popularity and are coming into wider use, the need for a mechanism that explains the system’s findings and recommendations becomes more critical. The system will also need a mechanism for ordering competing explanations. We examine two representative approaches to explanation in the literature - one due to G"ardenfors and one due to Pearl - and show that both suffer from significant problems. We propose an approach to defining a notion of “better explanation” that combines some of the features of both together with more recent work by Pearl and others on causality.