Globally interpretable models are a promising approach for trustworthy AI in safety-critical domains. Alongside global explanations, detailed local explanations are a crucial complement to effectively support human experts during inference. This work proposes the Calibrated Hierarchical QPM (CHiQPM) which offers uniquely comprehensive global and local interpretability, paving the way for human-AI complementarity. CHiQPM achieves superior global interpretability by contrastively explaining the majority of classes and offers novel hierarchical explanations that are more similar to how humans reason and can be traversed to offer a built-in interpretable Conformal prediction (CP) method. Our comprehensive evaluation shows that CHiQPM achieves state-of-the-art accuracy as a point predictor, maintaining 99% accuracy of non-interpretable models. This demonstrates a substantial improvement, where interpretability is incorporated without sacrificing overall accuracy. Furthermore, its calibrated set prediction is competitively efficient to other CP methods, while providing interpretable predictions of coherent sets along its hierarchical explanation.

翻译：全局可解释模型是在安全关键领域实现可信人工智能的一种有前景的方法。除了全局解释外，详细的局部解释对于在推理过程中有效支持人类专家也是至关重要的补充。本文提出了校准的分层QPM（CHiQPM），它提供了独特的全面全局和局部可解释性，为人类与人工智能的互补铺平了道路。CHiQPM通过对比性解释多数类别实现了卓越的全局可解释性，并提供了新颖的分层解释，这些解释更接近人类的推理方式，且可通过遍历获得内置的可解释共形预测（CP）方法。我们的综合评估表明，CHiQPM作为点预测器达到了最先进的准确率，保持了非可解释模型99%的准确率。这展示了显著的改进，即在保持总体准确率的同时融入了可解释性。此外，其校准的集合预测在效率上与其他CP方法具有竞争力，同时在其分层解释中提供了连贯集合的可解释预测。