View in EDS HTML Full Text PDF Full Text

Calibrating Reliance on Automated Advice: Transparency and Trust Calibration Feedback.

Saved in:
Bibliographic Details
Title: Calibrating Reliance on Automated Advice: Transparency and Trust Calibration Feedback.
Authors: Tatasciore, Monica (AUTHOR), Loft, Shayne (AUTHOR)
Source: International Journal of Human-Computer Interaction. Dec2025, Vol. 41 Issue 23, p14723-14733. 11p.
Subjects: Decision making, Decision support systems, Statistical reliability, User-centered system design, Psychological factors, Disclosure
Abstract: Inappropriate reliance on automated advice can result in humans accepting incorrect or rejecting correct advice. Increased automation transparency and trust calibration feedback are principles purported to promote accurate automation use. We examined the effects of automation transparency, trust calibration feedback, and their potential interacting effect on automation use accuracy and other outcomes. Participants completed uninhabited vehicle management missions by agreeing/disagreeing with automated advice. Transparency was manipulated within-subjects (low, high) and trust calibration feedback between-subjects (absent, present). If trust was inappropriate, trust calibration feedback instructed participants to take their time and carefully check display information. Higher transparency benefited automation use accuracy, decision time, perceived workload, trust, and usability. Trust calibration feedback had no benefit on automation use accuracy and subsequently did not amplify the benefits of increased transparency. These findings have potential implications to inform the design of automated decision aids to support human understanding of, and calibration to, automation capabilities. [ABSTRACT FROM AUTHOR]
Copyright of International Journal of Human-Computer Interaction is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Psychology and Behavioral Sciences Collection
Full text is not displayed to guests.
FullText Links:
  – Type: pdflink
Text:
  Availability: 1
Header DbId: pbh
DbLabel: Psychology and Behavioral Sciences Collection
An: 189570788
AccessLevel: 6
PubType: Academic Journal
PubTypeId: academicJournal
PreciseRelevancyScore: 0
IllustrationInfo
Items – Name: Title
  Label: Title
  Group: Ti
  Data: Calibrating Reliance on Automated Advice: Transparency and Trust Calibration Feedback.
– Name: Author
  Label: Authors
  Group: Au
  Data: <searchLink fieldCode="AR" term="%22Tatasciore%2C+Monica%22">Tatasciore, Monica</searchLink> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Loft%2C+Shayne%22">Loft, Shayne</searchLink> (AUTHOR)
– Name: TitleSource
  Label: Source
  Group: Src
  Data: <searchLink fieldCode="JN" term="%22International+Journal+of+Human-Computer+Interaction%22">International Journal of Human-Computer Interaction</searchLink>. Dec2025, Vol. 41 Issue 23, p14723-14733. 11p.
– Name: Subject
  Label: Subjects
  Group: Su
  Data: <searchLink fieldCode="DE" term="%22Decision+making%22">Decision making</searchLink><br /><searchLink fieldCode="DE" term="%22Decision+support+systems%22">Decision support systems</searchLink><br /><searchLink fieldCode="DE" term="%22Statistical+reliability%22">Statistical reliability</searchLink><br /><searchLink fieldCode="DE" term="%22User-centered+system+design%22">User-centered system design</searchLink><br /><searchLink fieldCode="DE" term="%22Psychological+factors%22">Psychological factors</searchLink><br /><searchLink fieldCode="DE" term="%22Disclosure%22">Disclosure</searchLink>
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: Inappropriate reliance on automated advice can result in humans accepting incorrect or rejecting correct advice. Increased automation transparency and trust calibration feedback are principles purported to promote accurate automation use. We examined the effects of automation transparency, trust calibration feedback, and their potential interacting effect on automation use accuracy and other outcomes. Participants completed uninhabited vehicle management missions by agreeing/disagreeing with automated advice. Transparency was manipulated within-subjects (low, high) and trust calibration feedback between-subjects (absent, present). If trust was inappropriate, trust calibration feedback instructed participants to take their time and carefully check display information. Higher transparency benefited automation use accuracy, decision time, perceived workload, trust, and usability. Trust calibration feedback had no benefit on automation use accuracy and subsequently did not amplify the benefits of increased transparency. These findings have potential implications to inform the design of automated decision aids to support human understanding of, and calibration to, automation capabilities. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
  Label:
  Group: Ab
  Data: <i>Copyright of International Journal of Human-Computer Interaction is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
PLink https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=pbh&AN=189570788
RecordInfo BibRecord:
  BibEntity:
    Identifiers:
      – Type: doi
        Value: 10.1080/10447318.2025.2487861
    Languages:
      – Code: eng
        Text: English
    PhysicalDescription:
      Pagination:
        PageCount: 11
        StartPage: 14723
    Subjects:
      – SubjectFull: Decision making
        Type: general
      – SubjectFull: Decision support systems
        Type: general
      – SubjectFull: Statistical reliability
        Type: general
      – SubjectFull: User-centered system design
        Type: general
      – SubjectFull: Psychological factors
        Type: general
      – SubjectFull: Disclosure
        Type: general
    Titles:
      – TitleFull: Calibrating Reliance on Automated Advice: Transparency and Trust Calibration Feedback.
        Type: main
  BibRelationships:
    HasContributorRelationships:
      – PersonEntity:
          Name:
            NameFull: Tatasciore, Monica
      – PersonEntity:
          Name:
            NameFull: Loft, Shayne
    IsPartOfRelationships:
      – BibEntity:
          Dates:
            – D: 01
              M: 12
              Text: Dec2025
              Type: published
              Y: 2025
          Identifiers:
            – Type: issn-print
              Value: 10447318
          Numbering:
            – Type: volume
              Value: 41
            – Type: issue
              Value: 23
          Titles:
            – TitleFull: International Journal of Human-Computer Interaction
              Type: main
ResultId 1