Network Analysis for Explanation

TL;DR

This paper presents a method for analyzing neural networks to extract key features influencing inference, and proposes a simple approach to generate explanations for AI decisions, enhancing explainability in safety-critical systems.

Contribution

It introduces a novel analysis technique for neural networks and a straightforward method for producing explanations of inference processes.

Findings

Identified features that primarily influence network inference.

Developed a simple explanation generation method.

Improved interpretability for safety-critical AI systems.

Abstract

Safety critical systems strongly require the quality aspects of artificial intelligence including explainability. In this paper, we analyzed a trained network to extract features which mainly contribute the inference. Based on the analysis, we developed a simple solution to generate explanations of the inference processes.