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.

该研究考察了可解释人工智能（XAI）在实际中的相关性，特别关注生产行业，并将其与当前学术XAI研究进行了比较。通过对多个行业中的各种职位和关键利益相关者进行广泛访谈，我们发现了XAI在当前工业实践中沿着机器学习（ML）生命周期的应用和未来的相关性。与当前研究方法相比，我们的访谈结果揭示出一些差异。虽然存在多种不同的XAI方法，但其中大多集中在模型评估阶段和数据科学家上。其他阶段的多样化功能目前要么未被充分探索，要么在实践者中不流行。与现有研究一致，我们的研究也确认了需要更多的努力来使非专业用户能够理解和解释不透明的AI模型，使用现有的方法和框架。

机器学习生命周期中可解释人工智能的千变万化：工业现实与研究现状