Exploring Local Explanation of Practical Industrial AI Applications: A Systematic Literature Review

Abstract

In recent years, numerous explainable artificial intelligence (XAI) use cases have been developed, to solve numerous real problems in industrial applications while maintaining the explainability level of the used artificial intelligence (AI) models to judge their quality and potentially hold the models accountable if they become corrupted. Therefore, understanding the state-of-the-art methods, pointing out recent issues, and deriving future directions are important to drive XAI research efficiently. This paper presents a systematic literature review of local explanation techniques and their practical applications in various industrial sectors. We first establish the need for XAI in response to opaque AI models and survey different local explanation methods for industrial AI applications. The number of studies is then examined with several factors, including industry sectors, AI models, data types, and XAI-based usage and purpose. We also look at the advantages and disadvantages of local explanation methods and how well they work in practical settings. The difficulties of using local explanation techniques are also covered, including computing complexity and the trade-off between precision and interpretability. Our findings demonstrate that local explanation techniques can boost industrial AI models’ transparency and interpretability and give insightful information about them. The efficiency of these procedures must be improved, and ethical concerns about their application must be resolved. This paper contributes to the increasing knowledge of local explanation strategies and offers guidance to academics and industry professionals who want to use these methods in practical settings.

Potential Applications of Explainable Artificial Intelligence to Actuarial Problems

“…In systematic reviews of XAI applied to other industries, we can find diverse application approaches, such as XAI methods and evaluation metrics related to different application domains and tasks focused on AI/ML applications and deep learning, concluding that more attention is required to generate explanations for general users in sensitive domains such as finance and the judicial system [12,13]. For example, the work [14] provides an overview of the trends in XAI and answers the question of accuracy versus explainability, considering the extent of human involvement and explanation assessment.…”

A Systematic Literature Review on Artificial Intelligence and Explainable Artificial Intelligence for Visual Quality Assurance in Manufacturing

“…• AI-and XAI-based methods adopted in the Industry Highlights the need for responsible and human-centric AI and XAI systems in industry applications, thus suggesting a focus for future research. [8] Scope:…”

“…To the best of our knowledge, this is the first comprehensive survey that delves into the application of AI and XAI methods in the context of VQA in manufacturing, thus encompassing a wide spectrum of practices, including visual quality control (VQC), process optimization, predictive maintenance, and root cause analysis. Prior research has already examined the use of AI for QA [5] and even explored local explanations [8]; however, these studies have only encompassed QC or predictive maintenance. Our survey distinguishes itself in two crucial aspects.…”