Currently, explainability represents a major barrier that Artificial Intelligence (AI) is facing in regard to its practical implementation in various application domains. To combat the lack of understanding of AI-based systems, Explainable AI (XAI) aims to make black-box AI models more transparent and comprehensible for humans. Fortunately, plenty of XAI methods have been introduced to tackle the explainability problem from different perspectives. However, due to the vast search space, it is challenging for ML practitioners and data scientists to start with the development of XAI software and to optimally select the most suitable XAI methods. To tackle this challenge, we introduce XAIR, a novel systematic metareview of the most promising XAI methods and tools. XAIR differentiates itself from existing reviews by aligning its results to the five steps of the software development process, including requirement analysis, design, implementation, evaluation, and deployment. Through this mapping, we aim to create a better understanding of the individual steps of developing XAI software and to foster the creation of real-world AI applications that incorporate explainability. Finally, we conclude with highlighting new directions for future research.

Visual quality inspection systems, crucial in sectors like manufacturing and logistics, employ computer vision and machine learning for precise, rapid defect detection. However, their unexplained nature can hinder trust, error identification, and system improvement. This paper presents a framework to bolster visual quality inspection by using CAM-based explanations to refine semantic segmentation models. Our approach consists of 1) Model Training, 2) XAI-based Model Explanation, 3) XAI Evaluation, and 4) Annotation Augmentation for Model Enhancement, informed by explanations and expert insights. Evaluations show XAI-enhanced models surpass original DeepLabv3-ResNet101 models, especially in intricate object segmentation.