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Dignity and use of algorithm in performance evaluation.

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Title: Dignity and use of algorithm in performance evaluation.
Authors: Zhang, Lixuan, Amos, Clinton
Source: Behaviour & Information Technology. Feb2024, Vol. 43 Issue 2, p401-418. 18p.
Subjects: Employee psychology, Prevention of employment discrimination, Human rights, Analysis of variance, Confidence intervals, Right to work (Human rights), Artificial intelligence, Descriptive statistics, Dignity, Respect, Algorithms, Employee reviews, Industrial relations
Geographic Terms: United States
Abstract: Algorithms are increasingly used by human resource departments to evaluate employee performance. While the algorithms are perceived to be objective and neutral by removing human biases, they are often perceived to be less fair than human managers. This research proposes dignity as an important construct in explaining the discrepancy in perceived fairness and investigates remedial steps for improving dignity and fairness for algorithm-based employee evaluations. Three experiments' results show that those evaluated by algorithms perceive lower levels of dignity, leading them to believe the process is less fair. In addition, we find that providing justifications for algorithm usage in employee evaluations improves perceived dignity. However, human-algorithm collaboration does not enhance perceived dignity. [ABSTRACT FROM AUTHOR]
Copyright of Behaviour & Information Technology 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
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  Data: Dignity and use of algorithm in performance evaluation.
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  Data: <searchLink fieldCode="AR" term="%22Zhang%2C+Lixuan%22">Zhang, Lixuan</searchLink><br /><searchLink fieldCode="AR" term="%22Amos%2C+Clinton%22">Amos, Clinton</searchLink>
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  Data: <searchLink fieldCode="JN" term="%22Behaviour+%26+Information+Technology%22">Behaviour & Information Technology</searchLink>. Feb2024, Vol. 43 Issue 2, p401-418. 18p.
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  Data: <searchLink fieldCode="DE" term="%22Employee+psychology%22">Employee psychology</searchLink><br /><searchLink fieldCode="DE" term="%22Prevention+of+employment+discrimination%22">Prevention of employment discrimination</searchLink><br /><searchLink fieldCode="DE" term="%22Human+rights%22">Human rights</searchLink><br /><searchLink fieldCode="DE" term="%22Analysis+of+variance%22">Analysis of variance</searchLink><br /><searchLink fieldCode="DE" term="%22Confidence+intervals%22">Confidence intervals</searchLink><br /><searchLink fieldCode="DE" term="%22Right+to+work+%28Human+rights%29%22">Right to work (Human rights)</searchLink><br /><searchLink fieldCode="DE" term="%22Artificial+intelligence%22">Artificial intelligence</searchLink><br /><searchLink fieldCode="DE" term="%22Descriptive+statistics%22">Descriptive statistics</searchLink><br /><searchLink fieldCode="DE" term="%22Dignity%22">Dignity</searchLink><br /><searchLink fieldCode="DE" term="%22Respect%22">Respect</searchLink><br /><searchLink fieldCode="DE" term="%22Algorithms%22">Algorithms</searchLink><br /><searchLink fieldCode="DE" term="%22Employee+reviews%22">Employee reviews</searchLink><br /><searchLink fieldCode="DE" term="%22Industrial+relations%22">Industrial relations</searchLink>
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  Data: Algorithms are increasingly used by human resource departments to evaluate employee performance. While the algorithms are perceived to be objective and neutral by removing human biases, they are often perceived to be less fair than human managers. This research proposes dignity as an important construct in explaining the discrepancy in perceived fairness and investigates remedial steps for improving dignity and fairness for algorithm-based employee evaluations. Three experiments' results show that those evaluated by algorithms perceive lower levels of dignity, leading them to believe the process is less fair. In addition, we find that providing justifications for algorithm usage in employee evaluations improves perceived dignity. However, human-algorithm collaboration does not enhance perceived dignity. [ABSTRACT FROM AUTHOR]
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  Data: <i>Copyright of Behaviour & Information Technology 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.)
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RecordInfo BibRecord:
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      – Type: doi
        Value: 10.1080/0144929X.2022.2164214
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      – Code: eng
        Text: English
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      – SubjectFull: Employee psychology
        Type: general
      – SubjectFull: Prevention of employment discrimination
        Type: general
      – SubjectFull: Human rights
        Type: general
      – SubjectFull: Analysis of variance
        Type: general
      – SubjectFull: Confidence intervals
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      – SubjectFull: Right to work (Human rights)
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      – SubjectFull: Artificial intelligence
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      – SubjectFull: Descriptive statistics
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      – SubjectFull: Dignity
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      – SubjectFull: Respect
        Type: general
      – SubjectFull: Algorithms
        Type: general
      – SubjectFull: Employee reviews
        Type: general
      – SubjectFull: Industrial relations
        Type: general
      – SubjectFull: United States
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      – TitleFull: Dignity and use of algorithm in performance evaluation.
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            NameFull: Zhang, Lixuan
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              Text: Feb2024
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              Y: 2024
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