为原型网络设计可验证的逻辑解释，让模型决策更可信
Formal Abductive Latent Explanations for Prototype-Based Networks
- 用形式化逻辑推导中间表征的充分条件，生成可靠解释
- 同一解释可能对应不同预测，现有方法存在误导风险
- 适用于图像分类任务，尤其适合安全关键场景
基于案例推理的网络通过输入与训练样本原型的相似性进行预测，其解释可直接指向影响结果的关键原型，因此被称作‘设计即可解释’。然而我们发现，此类解释有时具有误导性，因为多个不同输入可能产生相同解释却导致不同预测，限制了其在安全关键场景中的应用。受形式化可解释人工智能（FXAI）启发，我们提出消解式隐变量解释（ALEs），一种用于表达使预测成立的中间（隐含）表示的充分条件的形式框架。该方法融合了案例推理模型的内在可解释性与形式化XAI的严格保证。我们提出了无需求解器且可扩展的ALE生成算法，基于三种不同范式实现，并在多种数据集上验证了其在标准与细粒度图像分类任务中的可行性。相关代码见 https://github.com/julsoria/ale
原文摘要 · Abstract (English)
Case-based reasoning networks are machine-learning models that make predictions based on similarity between the input and prototypical parts of training samples, called prototypes. Such models are able to explain each decision by pointing to the prototypes that contributed the most to the final outcome. As the explanation is a core part of the prediction, they are often qualified as ``interpretable by design". While promising, we show that such explanations are sometimes misleading, which hampers their usefulness in safety-critical contexts. In particular, several instances may lead to different predictions and yet have the same explanation. Drawing inspiration from the field of formal eXplainable AI (FXAI), we propose Abductive Latent Explanations (ALEs), a formalism to express sufficient conditions on the intermediate (latent) representation of the instance that imply the prediction. Our approach combines the inherent interpretability of case-based reasoning models and the guarantees provided by formal XAI. We propose a solver-free and scalable algorithm for generating ALEs based on three distinct paradigms, compare them, and present the feasibility of our approach on diverse datasets for both standard and fine-grained image classification. The associated code can be found at https://github.com/julsoria/ale
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