Logic Explained Networks

TL;DR

Logic Explained Networks (LENs) are interpretable deep learning models that use human-understandable predicates and produce explanations in First-Order Logic, improving transparency without sacrificing classification performance.

Contribution

The paper introduces LENs, a novel neural network design that inherently provides logical explanations using simple predicates, bridging deep learning and explainability.

Findings

LENs outperform decision trees and Bayesian rule lists in classification accuracy.

LENs generate compact, human-understandable explanations.

LENs are effective in both supervised and unsupervised learning scenarios.

Abstract

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.