Negative performance feedback from algorithms or humans? effect of medical researchers' algorithm aversion on scientific misconduct.
Algorithm aversion
Algorithmic transparency
Egoism
Moral disengagement
Negative performance feedback
Scientific misconduct
Journal
BMC medical ethics
ISSN: 1472-6939
Titre abrégé: BMC Med Ethics
Pays: England
ID NLM: 101088680
Informations de publication
Date de publication:
23 Oct 2024
23 Oct 2024
Historique:
received:
23
08
2024
accepted:
17
10
2024
medline:
24
10
2024
pubmed:
24
10
2024
entrez:
24
10
2024
Statut:
epublish
Résumé
Institutions are increasingly employing algorithms to provide performance feedback to individuals by tracking productivity, conducting performance appraisals, and developing improvement plans, compared to traditional human managers. However, this shift has provoked considerable debate over the effectiveness and fairness of algorithmic feedback. This study investigates the effects of negative performance feedback (NPF) on the attitudes, cognition and behavior of medical researchers, comparing NPF from algorithms versus humans. Two scenario-based experimental studies were conducted with a total sample of 660 medical researchers (algorithm group: N1 = 411; human group: N2 = 249). Study 1 analyzes the differences in scientific misconduct, moral disengagement, and algorithmic attitudes between the two sources of NPF. The findings reveal that NPF from algorithms shows higher levels of moral disengagement, scientific misconduct, and negative attitudes towards algorithms compared to NPF from humans. Study 2, grounded in trait activation theory, investigates how NPF from algorithms triggers individual's egoism and algorithm aversion, potentially leading to moral disengagement and scientific misconduct. Results indicate that algorithm aversion triggers individuals' egoism, and their interaction enhances moral disengagement, which in turn leads to increased scientific misconduct among researchers. This relationship is also moderated by algorithmic transparency. The study concludes that while algorithms can streamline performance evaluations, they pose significant risks to scientific misconduct of researchers if not properly designed. These findings extend our understanding of NPF by highlighting the emotional and cognitive challenges algorithms face in decision-making processes, while also underscoring the importance of balancing technological efficiency with moral considerations to promote a healthy research environment. Moreover, managerial implications include integrating human oversight in algorithmic NPF processes and enhancing transparency and fairness to mitigate negative impacts on medical researchers' attitudes and behaviors.
Identifiants
pubmed: 39443942
doi: 10.1186/s12910-024-01121-0
pii: 10.1186/s12910-024-01121-0
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
118Subventions
Organisme : Cultivation for Young Top-notch Talents of Beijing Municipal Institutions
ID : BPHR202203241
Informations de copyright
© 2024. The Author(s).
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