Combining Sub-Symbolic and Symbolic Methods for Explainability
This addresses the problem of explainability in AI for users who are not experts, potentially improving trust and usability in domains like chemistry, though it appears incremental by building on existing sub-symbolic methods.
The paper tackles the lack of transparency in Graph Neural Networks (GNNs) by proposing a method that combines sub-symbolic and symbolic approaches to generate human-centric explanations, incorporating domain knowledge and causality, and shows its value and reliability in evaluation with a chemical dataset and ontology.
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.