Title: GNN Explanations that do not Explain and How to find Them
Primary area: interpretability and explainable AI
Keywords: graph neural networks, explainability, self-explainable, auditing, faithfulness

Abstract: Explanations provided by Self-explainable Graph Neural Networks (SE-GNNs) are fundamental for understanding the model's inner workings and for identifying potential misuse of sensitive attributes.
Although recent works have highlighted that these explanations can be suboptimal and potentially misleading, a characterization of their failure cases is unavailable.
In this work, we identify a critical failure of SE-GNN explanations: *explanations can be unambiguously unrelated to how the SE-GNNs infer labels.*
We show that, on the one hand, many SE-GNNs can achieve optimal true risk while producing these degenerate explanations, and on the other, most faithfulness metrics can fail to identify these failure modes.
Our empirical analysis reveals that degenerate explanations can be maliciously planted (allowing an attacker to hide the use of sensitive attributes) and can also emerge naturally, highlighting the need for reliable auditing.
To address this, we introduce a novel faithfulness metric that reliably marks degenerate explanations as unfaithful, in both malicious and natural settings.
Our code is available in the supplemental.

TL;DR: We found that Self-explainable Graph Neural Networks can output explanations that do not explain. We also found that faithfulness metrics can fail to detect these explanations. To solve this undetectability, we propose a benchmark and a new metric.