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@PHDTHESIS{Werz:1020690,
      author       = {Werz, Johanna Miriam},
      othercontributors = {Isenhardt, Ingrid and Ziefle, Martina},
      title        = {{K}ünstliche {I}ntelligenz erklären, verstehen, nutzen :
                      {A}nforderungen an {T}ransparenz und ihr {E}influss auf die
                      {N}utzung von {KI}-{E}ntscheidungsunterstützungssystemen},
      school       = {Rheinisch-Westfälische Technische Hochschule Aachen},
      type         = {Dissertation},
      address      = {Aachen},
      publisher    = {RWTH Aachen University},
      reportid     = {RWTH-2025-09177},
      pages        = {1 Online-Ressource : Illustrationen},
      year         = {2025},
      note         = {Veröffentlicht auf dem Publikationsserver der RWTH Aachen
                      University; Dissertation, Rheinisch-Westfälische Technische
                      Hochschule Aachen, 2025},
      abstract     = {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.},
      cin          = {735410},
      ddc          = {300},
      cid          = {$I:(DE-82)735410_20230123$},
      pnm          = {FAIRWork - Flexibilization of complex Ecosystems using
                      Democratic AI based Decision Support and Recommendation
                      Systems at Work (101069499) / OPSF654 - Transparency in
                      Artificial Intelligence: Considering Explainability, User
                      and System Factors (TAIGERS) (EXS-SF-OPSF654) / Exploratory
                      Research Space: Seed Fund (2) als Anschubfinanzierung zur
                      Erforschung neuer interdisziplinärer Ideen (EXS-SF) /
                      Excellence Strategy (EXS)},
      pid          = {G:(EU-Grant)101069499 / G:(DE-82)EXS-SF-OPSF654 /
                      G:(DE-82)EXS-SF / G:(DE-82)EXS},
      typ          = {PUB:(DE-HGF)11},
      doi          = {10.18154/RWTH-2025-09177},
      url          = {https://publications.rwth-aachen.de/record/1020690},
}