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%0 Thesis
%A Werz, Johanna Miriam
%T Künstliche Intelligenz erklären, verstehen, nutzen : Anforderungen an Transparenz und ihr Einfluss auf die Nutzung von KI-Entscheidungsunterstützungssystemen
%I Rheinisch-Westfälische Technische Hochschule Aachen
%V Dissertation
%C Aachen
%M RWTH-2025-09177
%P 1 Online-Ressource : Illustrationen
%D 2025
%Z Veröffentlicht auf dem Publikationsserver der RWTH Aachen University
%Z Dissertation, Rheinisch-Westfälische Technische Hochschule Aachen, 2025
%X Despite the increasing number of artificial intelligence (AI) systems for private usage, AI transparency has long been researched primarily from a technical perspective. However, study results with end users show that system transparency does not automatically lead to system acceptance. Therefore, the question arises of how transparency of AI decision support systems affects the use of these systems by end users. In this dissertation, this research question was investigated using three studies with a mixed-method approach. The first study, a quantitative online experiment with n = 169 participants, analyzed how accuracy information about an algorithm influences the use of this algorithm after an error. The second study, qualitative focus group discussions with n = 26 participants, identified requirements for AI transparency from the perspective of end users. The third study, a quantitative online experiment with n = 151 participants, compared four different types of transparency regarding their effect on trust and use of the respective algorithms. The results show that technical explanations alone are not sufficient to strengthen trust in AI systems or increase their usage. More than explanations of how an AI works, background information about developers, the motives of the institutions behind the AI or external audits help to build trust. Accuracy information has a limited positive effect on usage, while explanations about why a single result emerged are desirable when errors occur. The requirements towards AI transparency depend on the characteristics of the system, in particular how severe errors would be, and users' previous experience. More important than detailed transparency is ensuring that users understand the transparency measures and conveying the reliability of the AI-system. The work emphasizes the importance of a user-centered development of AI transparency due to the individuality of systems and user groups. In addition to further implications, a transparency matrix for developers was elaborated, which can be used to identify the necessary transparency implications based on given system characteristics. Implications also arise for political decision-makers to promote transparency in AI systems. In addition, limitations of the individual studies and the overall work are discussed and follow-up questions for further research are derived.
%F PUB:(DE-HGF)11
%9 Dissertation / PhD Thesis
%R 10.18154/RWTH-2025-09177
%U https://publications.rwth-aachen.de/record/1020690