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

TL;DR

This paper proposes a human-compatible explainable AI method that uses concept induction over background knowledge from Wikipedia to explain data differentials in a meaningful way.

Contribution

It introduces a novel approach applying formal logical concept induction with Wikipedia-based background knowledge to enhance XAI explanations.

Findings

Effective explanation of data differentials using concept induction

Utilization of Wikipedia category hierarchy as background knowledge

Meaningful explanations aligned with human understanding

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.