---
res:
  bibo_abstract:
  - Despite the widespread use of machine learning algorithms, their effectiveness
    is limited by a phenomenon known as algorithm aversion. Recent research concluded
    that unobserved variables can cause algorithm aversion. However, the impact of
    an unobserved variable on algorithm aversion remains unclear. Previous studies
    focused on situations where humans had more variables available than algorithms.
    We extend this research by conducting an online experiment with 94 participants,
    systematically varying the number of observable variables to the advisor and the
    advisor type. Surprisingly, our results did not confirm that an unobserved variable
    had a negative effect on advice-taking. Instead, we found a positive impact in
    an algorithm appreciation scenario. This study provides new insights into the
    paradoxical behavior in which people weigh advice more despite having fewer variables,
    as they correct for the advisor's errors. Practitioners should consider this behavior
    when designing algorithms and account for user correction behavior.@eng
  bibo_authorlist:
  - foaf_Person:
      foaf_givenName: Dirk
      foaf_name: Leffrang, Dirk
      foaf_surname: Leffrang
      foaf_workInfoHomepage: http://www.librecat.org/personId=51271
    orcid: 0000-0001-9004-2391
  bibo_issue: '19'
  dct_date: 2023^xs_gYear
  dct_language: eng
  dct_subject:
  - Algorithm aversion
  - Data
  - Decision-making
  - Advice-taking
  - Human-Computer Interaction
  dct_title: 'The Broken Leg of Algorithm Appreciation: An Experimental Study on the
    Effect of Unobserved Variables on Advice Utilization@'
...