@phdthesis{Wanner2022,
  author    = {Wanner, Jonas Paul},
  title     = {Artificial Intelligence for Human Decision-Makers: Systematization, Perception, and Adoption of Intelligent Decision Support Systems in Industry 4.0},
  doi       = {10.25972/OPUS-25901},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-259014},
  school      = {Universit{\"a}t W{\"u}rzburg},
  year      = {2022},
  abstract  = {Innovative possibilities for data collection, networking, and evaluation are unleashing previously untapped potential for industrial production. However, harnessing this potential also requires a change in the way we work. In addition to expanded automation, human-machine cooperation is becoming more important: The machine achieves a reduction in complexity for humans through artificial intelligence. In fractions of a second large amounts of data of high decision quality are analyzed and suggestions are offered. The human being, for this part, usually makes the ultimate decision. He validates the machine's suggestions and, if necessary, (physically) executes them. Both entities are highly dependent on each other to accomplish the task in the best possible way. Therefore, it seems particularly important to understand to what extent such cooperation can be effective. Current developments in the field of artificial intelligence show that research in this area is particularly focused on neural network approaches. These are considered to be highly powerful but have the disadvantage of lacking transparency. Their inherent computational processes and the respective result reasoning remain opaque to humans. Some researchers assume that human users might therefore reject the system's suggestions. 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. This work is intended to provide further insights relevant to the defined goal of XAI. 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. The focus is on socio-technical insights with the aim to better understand which factors are important for effective human-machine cooperation. The elaborations predominantly represent extended grounded research. Thus, the artifacts imply an extension of knowledge in order to develop and/ or test effective XAI methods and techniques based on this knowledge. Industry 4.0, with a focus on maintenance, is used as the context for this development.},
  subject      = {K{\"u}nstliche Intelligenz},
  language  = {en}
}