As artificial intelligence increasingly drives critical decisions, the ability to genuinely explain how neural networks make predictions is essential for trust. Yet, most current explanation methods offer post-hoc rationalizations rather than guaranteeing a true reflection of the model's reasoning. We introduce the notion of explanatory alignment, a requirement that explanations directly construct predictions rather than rationalize them. To achieve this in complex data domains, we present Pointwise-interpretable Networks (PiNets), a pseudo-linear architecture that forms linear models instance-wise. Evaluated on image classification and segmentation tasks, PiNets demonstrate that their explanations are deeply faithful across four criteria: meaningfulness, alignment, robustness, and sufficiency (MARS). Our contributions pave the way for promising avenues: by reconciling the predictive power of deep learning with the interpretability of linear models, PiNets provide a principled foundation for trustworthy AI and data-driven scientific discovery.

翻译：随着人工智能日益驱动关键决策，真正解释神经网络如何做出预测的能力对于建立信任至关重要。然而，当前大多数解释方法提供的是事后合理化解释，而非保证对模型推理的真实反映。我们提出了解释对齐的概念，即要求解释直接构建预测而非为其进行合理化。为了在复杂数据领域实现这一目标，我们提出了点可解释网络（PiNets），这是一种逐实例形成线性模型的伪线性架构。在图像分类和分割任务上的评估表明，PiNets的解释在四个标准上具有深度忠实性：有意义性、对齐性、鲁棒性和充分性（MARS）。我们的贡献为有前景的研究方向铺平了道路：通过调和深度学习的预测能力与线性模型的可解释性，PiNets为可信人工智能和数据驱动的科学发现提供了原则性基础。