  The large success of deep learning based methods in Visual Question Answering
(VQA) has concurrently increased the demand for explainable methods. Most
methods in Explainable Artificial Intelligence (XAI) focus on generating
post-hoc explanations rather than taking an intrinsic approach, the latter
characterizing an interpretable model. In this work, we introduce an
interpretable approach for graph-based VQA and demonstrate competitive
performance on the GQA dataset. This approach bridges the gap between
interpretability and performance. Our model is designed to intrinsically
produce a subgraph during the question-answering process as its explanation,
providing insight into the decision making. To evaluate the quality of these
generated subgraphs, we compare them against established post-hoc
explainability methods for graph neural networks, and perform a human
evaluation. Moreover, we present quantitative metrics that correlate with the
evaluations of human assessors, acting as automatic metrics for the generated
explanatory subgraphs. Our implementation is available at
https://github.com/DigitalPhonetics/Intrinsic-Subgraph-Generation-for-VQA.
