![]() ![]() The screening of novel materials with good performance and the modelling of quantitative structure-activity relationships (QSARs), among other issues, are hot topics in the field of materials science. Industry 4.0, with a focus on maintenance, is used as the context for this development. Thus, the artifacts imply an extension of knowledge in order to develop and/ or test effective XAI methods and techniques based on this knowledge. The elaborations predominantly represent extended grounded research. The focus is on socio-technical insights with the aim to better understand which factors are important for effective human-machine cooperation. For this purpose, artifacts are developed that represent research achievements regarding the systematization, perception, and adoption of artificially intelligent decision support systems from a user perspective. This work is intended to provide further insights relevant to the defined goal of XAI. The research domain of explainable artificial intelligence (XAI) addresses this problem and tries to develop methods to realize systems that are highly efficient and explainable. Some researchers assume that human users might therefore reject the system’s suggestions. ![]() Their inherent computational processes and the respective result reasoning remain opaque to humans. These are considered to be highly powerful but have the disadvantage of lacking transparency. ![]() Current developments in the field of artificial intelligence show that research in this area is particularly focused on neural network approaches. Therefore, it seems particularly important to understand to what extent such cooperation can be effective. Both entities are highly dependent on each other to accomplish the task in the best possible way. He validates the machine’s suggestions and, if necessary, (physically) executes them. The human being, for this part, usually makes the ultimate decision. In fractions of a second large amounts of data of high decision quality are analyzed and suggestions are offered. In addition to expanded automation, human-machine cooperation is becoming more important: The machine achieves a reduction in complexity for humans through artificial intelligence. However, harnessing this potential also requires a change in the way we work. Innovative possibilities for data collection, networking, and evaluation are unleashing previously untapped potential for industrial production. ![]()
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