Can I Trust the Explainer? Verifying Post-hoc Explanatory Methods
This work addresses the need for reliable explanations in AI to gain public trust, though it is incremental as it focuses on improving evaluation methods rather than proposing a new explainer.
The paper tackles the problem of verifying post-hoc explanatory methods for AI systems by identifying two issues: the incompatibility of different explanation perspectives and the inadequate validation of explainers on real-world neural networks. It introduces a verification framework based on a non-trivial neural network architecture with guarantees on inner workings, demonstrating failure modes of current explainers.
For AI systems to garner widespread public acceptance, we must develop methods capable of explaining the decisions of black-box models such as neural networks. In this work, we identify two issues of current explanatory methods. First, we show that two prevalent perspectives on explanations --- feature-additivity and feature-selection --- lead to fundamentally different instance-wise explanations. In the literature, explainers from different perspectives are currently being directly compared, despite their distinct explanation goals. The second issue is that current post-hoc explainers are either validated under simplistic scenarios (on simple models such as linear regression, or on models trained on syntactic datasets), or, when applied to real-world neural networks, explainers are commonly validated under the assumption that the learned models behave reasonably. However, neural networks often rely on unreasonable correlations, even when producing correct decisions. We introduce a verification framework for explanatory methods under the feature-selection perspective. Our framework is based on a non-trivial neural network architecture trained on a real-world task, and for which we are able to provide guarantees on its inner workings. We validate the efficacy of our evaluation by showing the failure modes of current explainers. We aim for this framework to provide a publicly available, off-the-shelf evaluation when the feature-selection perspective on explanations is needed.