Knowledge graphs for empirical concept retrieval

• URL: http://arxiv.org/abs/2404.07008v1
• Date: Wed, 10 Apr 2024 13:47:22 GMT
• Title: Knowledge graphs for empirical concept retrieval
• Authors: Lenka Tětková, Teresa Karen Scheidt, Maria Mandrup Fogh, Ellen Marie Gaunby Jørgensen, Finn Årup Nielsen, Lars Kai Hansen,
• Abstract summary: Concept-based explainable AI is promising as a tool to improve the understanding of complex models at the premises of a given user. Here, we present a workflow for user-driven data collection in both text and image domains. We test the retrieved concept datasets on two concept-based explainability methods, namely concept activation vectors (CAVs) and concept activation regions (CARs)
• Score: 1.06378109904813
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Concept-based explainable AI is promising as a tool to improve the understanding of complex models at the premises of a given user, viz.\ as a tool for personalized explainability. An important class of concept-based explainability methods is constructed with empirically defined concepts, indirectly defined through a set of positive and negative examples, as in the TCAV approach (Kim et al., 2018). While it is appealing to the user to avoid formal definitions of concepts and their operationalization, it can be challenging to establish relevant concept datasets. Here, we address this challenge using general knowledge graphs (such as, e.g., Wikidata or WordNet) for comprehensive concept definition and present a workflow for user-driven data collection in both text and image domains. The concepts derived from knowledge graphs are defined interactively, providing an opportunity for personalization and ensuring that the concepts reflect the user's intentions. We test the retrieved concept datasets on two concept-based explainability methods, namely concept activation vectors (CAVs) and concept activation regions (CARs) (Crabbe and van der Schaar, 2022). We show that CAVs and CARs based on these empirical concept datasets provide robust and accurate explanations. Importantly, we also find good alignment between the models' representations of concepts and the structure of knowledge graphs, i.e., human representations. This supports our conclusion that knowledge graph-based concepts are relevant for XAI.

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