Combining Sub-Symbolic and Symbolic Methods for Explainability

• URL: http://arxiv.org/abs/2112.01844v1
• Date: Fri, 3 Dec 2021 10:57:00 GMT
• Title: Combining Sub-Symbolic and Symbolic Methods for Explainability
• Authors: Anna Himmelhuber, Stephan Grimm, Sonja Zillner, Mitchell Joblin, Martin Ringsquandl and Thomas Runkler
• Abstract summary: A number of sub-symbolic approaches have been developed to provide insights into the GNN decision making process. These are first important steps on the way to explainability, but the generated explanations are often hard to understand for users that are not AI experts. We introduce a conceptual approach combining sub-symbolic and symbolic methods for human-centric explanations.
• Score: 1.3777144060953146
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Similarly to other connectionist models, Graph Neural Networks (GNNs) lack transparency in their decision-making. A number of sub-symbolic approaches have been developed to provide insights into the GNN decision making process. These are first important steps on the way to explainability, but the generated explanations are often hard to understand for users that are not AI experts. To overcome this problem, we introduce a conceptual approach combining sub-symbolic and symbolic methods for human-centric explanations, that incorporate domain knowledge and causality. We furthermore introduce the notion of fidelity as a metric for evaluating how close the explanation is to the GNN's internal decision making process. The evaluation with a chemical dataset and ontology shows the explanatory value and reliability of our method.

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