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In this paper, we investigate the practical relevance of explainableartificial intelligence (XAI) with a special focus on the producing industriesand relate them to the current state of academic XAI research. Our findings arebased on an extensive series of interviews regarding the role and applicabilityof XAI along the Machine Learning (ML) lifecycle in current industrial practiceand its expected relevance in the future. The interviews were conducted among agreat variety of roles and key stakeholders from different industry sectors. Ontop of that, we outline the state of XAI research by providing a concise reviewof the relevant literature. This enables us to provide an encompassing overviewcovering the opinions of the surveyed persons as well as the current state ofacademic research. By comparing our interview results with the current researchapproaches we reveal several discrepancies. While a multitude of different XAIapproaches exists, most of them are centered around the model evaluation phaseand data scientists. Their versatile capabilities for other stages arecurrently either not sufficiently explored or not popular among practitioners.In line with existing work, our findings also confirm that more efforts areneeded to enable also non-expert users' interpretation and understanding ofopaque AI models with existing methods and frameworks.

The Thousand Faces of Explainable AI Along the Machine Learning Life Cycle: Industrial Reality and Current State of Research