用双模型辩论方式解释图像分类器决策，更透明可信。
Free Argumentative Exchanges for Explaining Image Classifiers
- 设计双代理辩论框架，模拟对分类结果的争论。
- 在共识率与说服率上优于传统解释方法。
- 适合需要可解释性的AI应用开发者和研究者。
深度学习模型虽具强大图像分类能力，但其黑箱特性影响可信度。现有解释方法因模型复杂度高而难以准确呈现推理过程。本文提出一种名为自由辩论交换（FAX）的新框架，通过两个代理就图像属于某一类别展开辩论，实现对分类器决策的论证式解释。该框架允许代理以差异化方式内化对方观点。我们定义了共识率与说服率两个评估指标，实验表明FAX在这些指标上表现优异，且比传统非辩论式解释方法更忠实于原始模型。代码已开源：https://github.com/koriavinash1/FAX。
原文摘要 · Abstract (English)
Deep learning models are powerful image classifiers but their opacity hinders their trustworthiness. Explanation methods for capturing the reasoning process within these classifiers faithfully and in a clear manner are scarce, due to their sheer complexity and size. We provide a solution for this problem by defining a novel method for explaining the outputs of image classifiers with debates between two agents, each arguing for a particular class. We obtain these debates as concrete instances of Free Argumentative eXchanges (FAXs), a novel argumentation-based multi-agent framework allowing agents to internalise opinions by other agents differently than originally stated. We define two metrics (consensus and persuasion rate) to assess the usefulness of FAXs as argumentative explanations for image classifiers. We then conduct a number of empirical experiments showing that FAXs perform well along these metrics as well as being more faithful to the image classifiers than conventional, non-argumentative explanation methods. All our implementations can be found at https://github.com/koriavinash1/FAX.
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