In this paper, we investigate the practical relevance of explainable artificial intelligence (XAI) with a special focus on the producing industries and relate them to the current state of academic XAI research. Our findings are based on an extensive series of interviews regarding the role and applicability of XAI along the Machine Learning (ML) lifecycle in current industrial practice and its expected relevance in the future. The interviews were conducted among a great variety of roles and key stakeholders from different industry sectors. On top of that, we outline the state of XAI research by providing a concise review of the relevant literature. This enables us to provide an encompassing overview covering the opinions of the surveyed persons as well as the current state of academic research. By comparing our interview results with the current research approaches we reveal several discrepancies. While a multitude of different XAI approaches exists, most of them are centered around the model evaluation phase and data scientists. Their versatile capabilities for other stages are currently either not sufficiently explored or not popular among practitioners. In line with existing work, our findings also confirm that more efforts are needed to enable also non-expert users' interpretation and understanding of opaque AI models with existing methods and frameworks.

翻译：本文研究了可解释人工智能（XAI）在工业生产领域的实际应用关联性，并与学术界当前的研究进展进行对比分析。研究基于对机器学习生命周期中XAI在现行工业实践中的角色定位、适用性及未来预期价值的系列深度访谈。访谈对象涵盖不同行业部门的多元角色与关键利益相关者。在此基础上，我们通过系统梳理相关文献勾勒出XAI研究全景图，从而形成涵盖受访者观点与学术研究现状的综合性综述。通过将访谈结果与现有研究方法进行对比，我们发现若干脱节现象：尽管存在多种XAI方法，但多数聚焦于模型评估阶段与数据科学家群体，其在不同生命周期阶段的多样化应用潜力尚未得到充分挖掘或未获从业者广泛认知。与现有研究结论一致，本研究证实需要投入更多努力，使非专业用户能够通过现有方法与框架理解与解读不透明AI模型。