Towards Human-Understandable Visual Recognition for Nonexperts in Industrial Inspection: A Case Study for Car Manufacturing Lines ^*

Despite growing interest in attribution-based eX-plainable Artificial Intelligence (XAI) methods, existing tools designed by Artificial Intelligence (AI) experts often overlook the needs of end users such as Blue Collar Workers (BCW), increasing environmental and financial risks for companies relying on them for critical decisions in industrial inspection. In this work, we aim to understand BCW needs in car manufacturing lines and improve the transparency and trustworthiness of visual recognition tools. We evaluate existing XAI methods, such as Shapley values and saliency maps, and explore how enhancing them with Large Multimodal Model (LMM)s can help bridge the gap between model explanations and human intuition. Our goal is to provide explanations that better align with the needs of nonexpert users. Our hypothesis is validated through a custom-designed user study involving 20 nonexpert users, specifically addressing two key use cases: nut welding quality assurance in body-in-white assembly and part carrier inspection for assembly line readiness. The results demonstrate that humans, assisted by our XAI methods, satisfaction and efficiency increased in use cases consisting of object detection or classification. These findings suggest that augmenting attribution-based methods with LMM explanations offers great potential for human-understandable XAI interfaces in automotive manufacturing, reducing the risks of over- or under-trusting AI in critical production settings.