Towards Human-Compatible XAI: Explaining Data Differentials with Concept Induction over Background Knowledge

• URL: http://arxiv.org/abs/2209.13710v1
• Date: Tue, 27 Sep 2022 21:51:27 GMT
• Title: Towards Human-Compatible XAI: Explaining Data Differentials with Concept Induction over Background Knowledge
• Authors: Cara Widmer, Md Kamruzzaman Sarker, Srikanth Nadella, Joshua Fiechter, Ion Juvina, Brandon Minnery, Pascal Hitzler, Joshua Schwartz, Michael Raymer
• Abstract summary: We show that concept induction can be used to explain data differentials in the context of Explainable AI (XAI) Our approach utilizes a large class hierarchy, curated from the Wikipedia category hierarchy, as background knowledge.
• Score: 2.803567242358594
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Concept induction, which is based on formal logical reasoning over description logics, has been used in ontology engineering in order to create ontology (TBox) axioms from the base data (ABox) graph. In this paper, we show that it can also be used to explain data differentials, for example in the context of Explainable AI (XAI), and we show that it can in fact be done in a way that is meaningful to a human observer. Our approach utilizes a large class hierarchy, curated from the Wikipedia category hierarchy, as background knowledge.

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