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Automated face recognition assists with low‐prevalence face identity mismatches but can bias users.

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Title: Automated face recognition assists with low‐prevalence face identity mismatches but can bias users.
Authors: Mueller, Melina (AUTHOR), Hancock, Peter J. B. (AUTHOR), Cunningham, Emily K. (AUTHOR), Watt, Roger J. (AUTHOR), Carragher, Daniel (AUTHOR), Bobak, Anna K. (AUTHOR)
Source: British Journal of Psychology. May2026, Vol. 117 Issue 2, p567-584. 18p.
Subjects: Pearson correlation (Statistics), T-test (Statistics), Data analysis, Research funding, Artificial intelligence, Questionnaires, Decision making, Descriptive statistics, Research bias, Artificial neural networks, Analysis of variance, Statistics, Data analysis software, Confidence intervals, Face perception, User interfaces, Regression analysis
Abstract: We present three experiments to study the effects of giving information about the decision of an automated face recognition (AFR) system to participants attempting to decide whether two face images show the same person. We make three contributions designed to make our results applicable to real‐word use: participants are given the true response of a highly accurate AFR system; the face set reflects the mixed ethnicity of the city of London from where participants are drawn; and there are only 10% of mismatches. Participants were equally accurate when given the similarity score of the AFR system or just the binary decision but shifted their bias towards match and were over‐confident on difficult pairs when given only binary information. No participants achieved the 100% accuracy of the AFR system, and they had only weak insight about their own performance. [ABSTRACT FROM AUTHOR]
Copyright of British Journal of Psychology is the property of Wiley-Blackwell 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
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  Data: Automated face recognition assists with low‐prevalence face identity mismatches but can bias users.
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  Data: <searchLink fieldCode="AR" term="%22Mueller%2C+Melina%22">Mueller, Melina</searchLink> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Hancock%2C+Peter+J%2E+B%2E%22">Hancock, Peter J. B.</searchLink> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Cunningham%2C+Emily+K%2E%22">Cunningham, Emily K.</searchLink> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Watt%2C+Roger+J%2E%22">Watt, Roger J.</searchLink> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Carragher%2C+Daniel%22">Carragher, Daniel</searchLink> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Bobak%2C+Anna+K%2E%22">Bobak, Anna K.</searchLink> (AUTHOR)
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  Data: <searchLink fieldCode="JN" term="%22British+Journal+of+Psychology%22">British Journal of Psychology</searchLink>. May2026, Vol. 117 Issue 2, p567-584. 18p.
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  Data: <searchLink fieldCode="DE" term="%22Pearson+correlation+%28Statistics%29%22">Pearson correlation (Statistics)</searchLink><br /><searchLink fieldCode="DE" term="%22T-test+%28Statistics%29%22">T-test (Statistics)</searchLink><br /><searchLink fieldCode="DE" term="%22Data+analysis%22">Data analysis</searchLink><br /><searchLink fieldCode="DE" term="%22Research+funding%22">Research funding</searchLink><br /><searchLink fieldCode="DE" term="%22Artificial+intelligence%22">Artificial intelligence</searchLink><br /><searchLink fieldCode="DE" term="%22Questionnaires%22">Questionnaires</searchLink><br /><searchLink fieldCode="DE" term="%22Decision+making%22">Decision making</searchLink><br /><searchLink fieldCode="DE" term="%22Descriptive+statistics%22">Descriptive statistics</searchLink><br /><searchLink fieldCode="DE" term="%22Research+bias%22">Research bias</searchLink><br /><searchLink fieldCode="DE" term="%22Artificial+neural+networks%22">Artificial neural networks</searchLink><br /><searchLink fieldCode="DE" term="%22Analysis+of+variance%22">Analysis of variance</searchLink><br /><searchLink fieldCode="DE" term="%22Statistics%22">Statistics</searchLink><br /><searchLink fieldCode="DE" term="%22Data+analysis+software%22">Data analysis software</searchLink><br /><searchLink fieldCode="DE" term="%22Confidence+intervals%22">Confidence intervals</searchLink><br /><searchLink fieldCode="DE" term="%22Face+perception%22">Face perception</searchLink><br /><searchLink fieldCode="DE" term="%22User+interfaces%22">User interfaces</searchLink><br /><searchLink fieldCode="DE" term="%22Regression+analysis%22">Regression analysis</searchLink>
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: We present three experiments to study the effects of giving information about the decision of an automated face recognition (AFR) system to participants attempting to decide whether two face images show the same person. We make three contributions designed to make our results applicable to real‐word use: participants are given the true response of a highly accurate AFR system; the face set reflects the mixed ethnicity of the city of London from where participants are drawn; and there are only 10% of mismatches. Participants were equally accurate when given the similarity score of the AFR system or just the binary decision but shifted their bias towards match and were over‐confident on difficult pairs when given only binary information. No participants achieved the 100% accuracy of the AFR system, and they had only weak insight about their own performance. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
  Label:
  Group: Ab
  Data: <i>Copyright of British Journal of Psychology is the property of Wiley-Blackwell 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=192785884
RecordInfo BibRecord:
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    Identifiers:
      – Type: doi
        Value: 10.1111/bjop.12745
    Languages:
      – Code: eng
        Text: English
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      Pagination:
        PageCount: 18
        StartPage: 567
    Subjects:
      – SubjectFull: Pearson correlation (Statistics)
        Type: general
      – SubjectFull: T-test (Statistics)
        Type: general
      – SubjectFull: Data analysis
        Type: general
      – SubjectFull: Research funding
        Type: general
      – SubjectFull: Artificial intelligence
        Type: general
      – SubjectFull: Questionnaires
        Type: general
      – SubjectFull: Decision making
        Type: general
      – SubjectFull: Descriptive statistics
        Type: general
      – SubjectFull: Research bias
        Type: general
      – SubjectFull: Artificial neural networks
        Type: general
      – SubjectFull: Analysis of variance
        Type: general
      – SubjectFull: Statistics
        Type: general
      – SubjectFull: Data analysis software
        Type: general
      – SubjectFull: Confidence intervals
        Type: general
      – SubjectFull: Face perception
        Type: general
      – SubjectFull: User interfaces
        Type: general
      – SubjectFull: Regression analysis
        Type: general
    Titles:
      – TitleFull: Automated face recognition assists with low‐prevalence face identity mismatches but can bias users.
        Type: main
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          Name:
            NameFull: Mueller, Melina
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            NameFull: Hancock, Peter J. B.
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            NameFull: Cunningham, Emily K.
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            NameFull: Watt, Roger J.
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            NameFull: Carragher, Daniel
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            NameFull: Bobak, Anna K.
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          Dates:
            – D: 01
              M: 05
              Text: May2026
              Type: published
              Y: 2026
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              Value: 117
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            – TitleFull: British Journal of Psychology
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