Combining Sub-Symbolic and Symbolic Methods for Explainability

@article{Himmelhuber2021CombiningSA,
  title={Combining Sub-Symbolic and Symbolic Methods for Explainability},
  author={Anna Himmelhuber and Stephan Grimm and Sonja Zillner and Mitchell Joblin and Martin Ringsquandl and Thomas A. Runkler},
  journal={ArXiv},
  year={2021},
  volume={abs/2112.01844}
}
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… 
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