---
res:
  bibo_abstract:
  - Explainable AI (XAI) provides approaches and techniques for building trust in
    AI models. This paper presents and explores XAI approaches focusing on user interface
    concepts in predictive maintenance. The underlying AI model is based on an open
    dataset for wind turbines. An enhanced multi-class self-conceived labeling strategy
    improves the model and, thus, supports the XAI approaches. Previous research in
    user-centered XAI shows that users do not exploit the possibilities of XAI methods
    and instead rely on their intuition. To counter this tendency, we present user
    interfaces incorporating gamification elements to enhance understanding of AI
    outputs. We highlight our approach via two examples, demonstrating a local and
    a global XAI technique respectively. A preliminary user study was conducted to
    assess the value added by these gamification aspects. While the findings were
    inconclusive, they provided an initial insight into the potential of these design
    elements to foster user engagement in the realm of XAI.@eng
  bibo_authorlist:
  - foaf_Person:
      foaf_givenName: Valentin
      foaf_name: Grimm, Valentin
      foaf_surname: Grimm
      foaf_workInfoHomepage: http://www.librecat.org/personId=74000
  - foaf_Person:
      foaf_givenName: Jonas
      foaf_name: Potthast, Jonas
      foaf_surname: Potthast
      foaf_workInfoHomepage: http://www.librecat.org/personId=81287
  - foaf_Person:
      foaf_givenName: Jessica
      foaf_name: Rubart, Jessica
      foaf_surname: Rubart
      foaf_workInfoHomepage: http://www.librecat.org/personId=45672
    orcid: 0000-0003-0937-3551
  bibo_doi: 10.1109/INDIN51400.2023.10217864
  dct_date: 2023^xs_gYear
  dct_language: eng
  dct_publisher: IEEE@
  dct_subject:
  - XAI
  - Industrial Analytics
  - Motivational Exploration
  - SHAP
  dct_title: Motivational Exploration of Explanations in Industrial Analytics@
...