Model interpretability plays a central role in human-AI decision-making systems. Ideally, explanations should be expressed using human-interpretable semantic concepts. Moreover, the causal relations between these concepts should be captured by the explainer to allow for reasoning about the explanations. Lastly, explanation methods should be efficient and not compromise the performance of the predictive task. Despite the rapid advances in AI explainability in recent years, as far as we know to date, no method fulfills these three properties. Indeed, mainstream methods for local concept explainability do not produce causal explanations and incur a trade-off between explainability and prediction performance. We present DiConStruct, an explanation method that is both concept-based and causal, with the goal of creating more interpretable local explanations in the form of structural causal models and concept attributions. Our explainer works as a distillation model to any black-box machine learning model by approximating its predictions while producing the respective explanations. Because of this, DiConStruct generates explanations efficiently while not impacting the black-box prediction task. We validate our method on an image dataset and a tabular dataset, showing that DiConStruct approximates the black-box models with higher fidelity than other concept explainability baselines, while providing explanations that include the causal relations between the concepts.

翻译：模型可解释性在人机协同决策系统中扮演着核心角色。理想情况下，解释应使用人类可理解的语义概念进行表达，同时解释器需捕捉这些概念间的因果关系以支持推理。此外，解释方法需保持高效性且不牺牲预测任务的性能。尽管近年来AI可解释性研究发展迅速，但据我们所知，目前尚无方法同时满足上述三个特性：主流局部概念解释方法既无法生成因果解释，又在可解释性与预测性能之间存在权衡。本文提出DiConStruct——一种兼具概念基础与因果特性的解释方法，旨在通过结构因果模型与概念归因的形式生成更具可解释性的局部解释。我们的解释器作为蒸馏模型，通过逼近任意黑箱机器学习模型的预测结果来生成相应解释，因此既能够高效产生解释，又不影响黑箱预测任务。我们在图像数据集和表格数据集上验证了该方法，实验表明DiConStruct在逼近黑箱模型时具有优于其他概念解释基线的保真度，同时其生成的解释能够涵盖概念间的因果关系。