---
res:
  bibo_abstract:
  - 'While the importance of explainable artificial intelligence in high-stakes decision-making
    is widely recognized in existing literature, empirical studies assessing users''
    perceived value of explanations are scarce. In this paper, we aim to address this
    shortcoming by conducting an empirical study focused on measuring the perceived
    value of the following types of explanations: plain explanations based on feature
    attribution, counterfactual explanations and complex counterfactual explanations.
    We measure an explanation''s value using five dimensions: perceived accuracy,
    understandability, plausibility, sufficiency of detail, and user satisfaction.
    Our findings indicate a sweet spot of explanation complexity, with both dimensional
    and structural complexity positively impacting the perceived value up to a certain
    threshold.@eng'
  bibo_authorlist:
  - foaf_Person:
      foaf_givenName: Felix
      foaf_name: Liedeker, Felix
      foaf_surname: Liedeker
      foaf_workInfoHomepage: http://www.librecat.org/personId=93275
  - foaf_Person:
      foaf_givenName: Christoph
      foaf_name: Düsing, Christoph
      foaf_surname: Düsing
  - foaf_Person:
      foaf_givenName: Marcel
      foaf_name: Nieveler, Marcel
      foaf_surname: Nieveler
  - foaf_Person:
      foaf_givenName: Philipp
      foaf_name: Cimiano, Philipp
      foaf_surname: Cimiano
  dct_date: 2024^xs_gYear
  dct_language: eng
  dct_subject:
  - XAI
  - Explanation Complexity
  - User Perception
  dct_title: 'An Empirical Investigation of Users'' Assessment of XAI Explanations:
    Identifying the Sweet-Spot of Explanation Complexity@'
...