The large and still increasing popularity of deep learning clashes with a major limit of neural network architectures, that consists in their lack of capability in providing human-understandable motivations of their decisions. In situations in which the machine is expected to support the decision of human experts, providing a comprehensible explanation is a feature of crucial importance. The language used to communicate the explanations must be formal enough to be implementable in a machine and friendly enough to be understandable by a wide audience. In this paper, we propose a general approach to Explainable Artificial Intelligence in the case of neural architectures, showing how a mindful design of the networks leads to a family of interpretable deep learning models called Logic Explained Networks (LENs). LENs only require their inputs to be human-understandable predicates, and they provide explanations in terms of simple First-Order Logic (FOL) formulas involving such predicates. LENs are general enough to cover a large number of scenarios. Amongst them, we consider the case in which LENs are directly used as special classifiers with the capability of being explainable, or when they act as additional networks with the role of creating the conditions for making a black-box classifier explainable by FOL formulas. Despite supervised learning problems are mostly emphasized, we also show that LENs can learn and provide explanations in unsupervised learning settings. Experimental results on several datasets and tasks show that LENs may yield better classifications than established white-box models, such as decision trees and Bayesian rule lists, while providing more compact and meaningful explanations.
翻译:深度学习与神经网络结构存在重大限制的深度学习冲突,其广度和日益受欢迎程度,在于它们缺乏提供其决定的人类可理解的动机的能力。在机器预期支持人类专家的决定的情况下,提供可理解的解释是一个至关重要的特点。用来传达解释的语文必须足够正式,足以在一个机器中执行,而且友好度足以让广大受众理解。在神经结构中,我们提出了一个解释人工智能的一般方法,表明网络的谨慎设计如何导致一个可解释的深层次学习模型的大家庭,称为“逻辑解释网络 ” (LENs )。在机器预期支持人类专家的决定的情况下,提供可理解的解释是一个至关重要的特点。语言只需他们的投入是人所无法理解的前提,它们用简单的“一极逻辑”(FOL)公式来解释。LENs很一般,足以涵盖大量的情况。其中,我们认为,在神经网络直接用作特殊的分类,具有解释性的能力,或者当他们的行为以最能理解的“逻辑解释”解释性解释性解释性模型时,我们也可以以“Lendor”的模型来解释更多的学习。