On the Value of Labeled Data and Symbolic Methods for Hidden Neuron Activation Analysis

TL;DR

This paper introduces a novel, model-agnostic explainable AI method that uses a large concept hierarchy and OWL-reasoning to interpret hidden neuron activations in deep neural networks, enhancing understanding of internal representations.

Contribution

The paper presents a new symbolic, background knowledge-based approach for interpreting hidden neuron activations, demonstrating its effectiveness compared to existing explainable AI methods.

Findings

Automatically attaches meaningful class expressions to neurons

Provides competitive explanations in both quantitative and qualitative evaluations

Utilizes a Wikipedia-derived concept hierarchy with OWL-reasoning for explanation generation

Abstract

A major challenge in Explainable AI is in correctly interpreting activations of hidden neurons: accurate interpretations would help answer the question of what a deep learning system internally detects as relevant in the input, demystifying the otherwise black-box nature of deep learning systems. The state of the art indicates that hidden node activations can, in some cases, be interpretable in a way that makes sense to humans, but systematic automated methods that would be able to hypothesize and verify interpretations of hidden neuron activations are underexplored. This is particularly the case for approaches that can both draw explanations from substantial background knowledge, and that are based on inherently explainable (symbolic) methods. In this paper, we introduce a novel model-agnostic post-hoc Explainable AI method demonstrating that it provides meaningful interpretations. Our approach is based on using a Wikipedia-derived concept hierarchy with approximately 2 million classes as background knowledge, and utilizes OWL-reasoning-based Concept Induction for explanation generation. Additionally, we explore and compare the capabilities of off-the-shelf pre-trained multimodal-based explainable methods. Our results indicate that our approach can automatically attach meaningful class expressions as explanations to individual neurons in the dense layer of a Convolutional Neural Network. Evaluation through statistical analysis and degree of concept activation in the hidden layer show that our method provides a competitive edge in both quantitative and qualitative aspects compared to prior work.