Explainable artificial intelligence (XAI) aims to make machine learning models more transparent. While many approaches focus on generating explanations post-hoc, interpretable approaches, which generate the explanations intrinsically alongside the predictions, are relatively rare. In this work, we integrate different discrete subset sampling methods into a graph-based visual question answering system to compare their effectiveness in generating interpretable explanatory subgraphs intrinsically. We evaluate the methods on the GQA dataset and show that the integrated methods effectively mitigate the performance trade-off between interpretability and answer accuracy, while also achieving strong co-occurrences between answer and question tokens. Furthermore, we conduct a human evaluation to assess the interpretability of the generated subgraphs using a comparative setting with the extended Bradley-Terry model, showing that the answer and question token co-occurrence metrics strongly correlate with human preferences. Our source code is publicly available.

翻译：可解释人工智能（XAI）旨在提升机器学习模型的透明度。尽管许多方法侧重于后验生成解释，但能够在预测过程中同步生成解释的可解释方法相对较少。本研究将多种离散子集采样方法集成于基于图的视觉问答系统，以比较其在同步生成可解释性子图方面的效能。我们在GQA数据集上评估了这些方法，结果表明集成方法有效缓解了可解释性与答案准确性之间的性能权衡，同时实现了答案与问题词汇间的高度共现。此外，我们通过扩展布拉德利-特里模型的对比设置进行人工评估，以检验生成子图的可解释性，结果显示答案与问题词汇的共现指标与人类偏好高度相关。本研究源代码已公开。