Automated face recognition assists with low‐prevalence face identity mismatches but can bias users.
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...
| Published in: | British Journal of Psychology Vol. 117; no. 2; pp. 567 - 585 |
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| Main Authors: | , , , , , |
| Format: | Article |
| Published: |
Wiley-Blackwell
May2026
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| Subjects: | |
| Online Access: | View this record in EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ssf&AN=192785884&site=ehost-live header: @attributes: shortDbName: ssf uiTerm: 192785884 longDbName: Social Sciences Full Text (H.W. Wilson) uiTag: AN controlInfo: bkinfo: jinfo: jid: 00071269 BJP jtl: British Journal of Psychology issn: 00071269 maglogo: Y pubinfo: dt: May2026 vid: 117 iid: 2 pid: 480 pub: Wiley-Blackwell artinfo: ui: 192785884 10.1111/bjop.12745 ppf: 567 ppct: 18 formats: fmt: – @attributes: type: T – @attributes: type: C – @attributes: type: P size: 797KB tig: atl: Automated face recognition assists with low‐prevalence face identity mismatches but can bias users. aug: au: Mueller, Melina Hancock, Peter J. B. Cunningham, Emily K. Watt, Roger J. Carragher, Daniel Bobak, Anna K. affil: Psychology, Faculty of Natural Sciences, University of Stirling, Stirling, UK School of Psychology, Faculty of Health and Medical Sciences, University of Adelaide, Adelaide South Australia,, Australia su: Artificial intelligence Decision making Analysis of variance Pearson correlation (Statistics) T-test (Statistics) Data analysis Research funding Questionnaires Descriptive statistics Research bias Artificial neural networks Statistics Data analysis software Confidence intervals Face perception User interfaces Regression analysis sug: subj: Artificial intelligence Decision making Analysis of variance Pearson correlation (Statistics) T-test (Statistics) Data analysis Research funding Questionnaires Descriptive statistics Research bias Artificial neural networks Statistics Data analysis software Confidence intervals Face perception User interfaces Regression analysis keyword: attitudes towards AI automated face recognition decision making deep neural networks face matching face recognition attitudes towards AI automated face recognition decision making deep neural networks face matching face recognition ab: 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. pubtype: Academic Journal doctype: Article src: R language: English refInfo: copyright: @attributes: flag: N holdings: @attributes: islocal: N |
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