Feature based local attribution methods are amongst the most prevalent in explainable artificial intelligence (XAI) literature. Going beyond standard correlation, recently, methods have been proposed that highlight what should be minimally sufficient to justify the classification of an input (viz. pertinent positives). While minimal sufficiency is an attractive property, the resulting explanations are often too sparse for a human to understand and evaluate the local behavior of the model, thus making it difficult to judge its overall quality. To overcome these limitations, we propose a novel method called Path-Sufficient Explanations Method (PSEM) that outputs a sequence of sufficient explanations for a given input of strictly decreasing size (or value) -- from original input to a minimally sufficient explanation -- which can be thought to trace the local boundary of the model in a smooth manner, thus providing better intuition about the local model behavior for the specific input. We validate these claims, both qualitatively and quantitatively, with experiments that show the benefit of PSEM across all three modalities (image, tabular and text). A user study depicts the strength of the method in communicating the local behavior, where (many) users are able to correctly determine the prediction made by a model.
翻译:以本地特性为基础的归因方法在可解释的人工智能(XAI)文献中最为普遍。最近,除了标准相关性外,还提出了一些方法,强调什么应该最起码地足以证明对输入(相关正数)进行分类是合理的。虽然最起码的充足性是一种有吸引力的属性,但由此产生的解释往往太少,人类无法理解和评价模型的当地行为,因此难以判断其总体质量。为了克服这些限制,我们提议了一种新颖的方法,称为“路径-足量解释方法(PSEM)”,为从原始输入到最充分的解释(从原始输入到最起码的足够解释)的某个输入提供了足够解释的顺序,这些解释可以被认为能够顺利地追踪模型的当地边界,从而对具体输入的当地模型行为提供更好的直觉。我们用实验来验证这些说法,质量和数量两方面都表明PSEM在所有三种模式(图像、表格和文本)中都具有益处。用户研究说明了在(many)用户能够正确确定模型预测的地方行为的方法的力度。