An analysis of explainability methods for convolutional neural networks. (January 2023)

• Record Type: Journal Article
• Authors: Haar, Lynn Vonder; Elvira, Timothy; Ochoa, Omar
• Is Part Of: Engineering applications of artificial intelligence. Volume 117:Part A(2023)
• Publication Date: 2023-01
• Subjects: Explainability -- Black box model -- Convolutional neural network -- Image recognition -- High-risk fields -- Safety–critical fields; Engineering -- Data processing -- Periodicals; Artificial intelligence -- Periodicals; Expert systems (Computer science) -- Periodicals; Ingénierie -- Informatique -- Périodiques; Intelligence artificielle -- Périodiques; Systèmes experts (Informatique) -- Périodiques; Artificial intelligence; Engineering -- Data processing; Expert systems (Computer science); Periodicals; 620.00285
• DOI: 10.1016/j.engappai.2022.105606
• Languages: English
• ISSNs: 0952-1976

Abstract

Abstract: Deep learning models have gained a reputation of high accuracy in many domains. Convolutional Neural Networks (CNN) are specialized towards image recognition and have high accuracy in classifying objects within images. However, CNNs are an example of a black box model, meaning that experts are unsure how they work internally to reach a classification decision. Without knowing the reasoning behind a decision, there is low confidence that CNNs will continue to make accurate decisions, so it is unsafe to use them in high-risk or safety–critical​ fields without first developing methods to explain their decisions. This paper is a survey and analysis of the available explainability methods for showing the reasoning behind CNN decisions.