医学研究人员对算法的厌恶对科学不端行为的影响?

IF 3 1区 哲学 Q1 ETHICS
Ganli Liao, Feiwen Wang, Wenhui Zhu, Qichao Zhang
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引用次数: 0

摘要

与传统的人力管理相比,各机构正越来越多地采用算法向个人提供绩效反馈,包括跟踪生产率、进行绩效评估和制定改进计划。然而,这种转变引发了关于算法反馈的有效性和公平性的大量争论。本研究通过比较算法和人类的负面绩效反馈(NPF),调查了负面绩效反馈对医学研究人员的态度、认知和行为的影响。研究人员对总共 660 名医学研究人员(算法组:N1 = 411;人类组:N2 = 249)进行了两项基于场景的实验研究。研究 1 分析了两种 NPF 来源在科学不端行为、道德脱离和算法态度方面的差异。研究结果显示,与来自人类的 NPF 相比,来自算法的 NPF 表现出更高水平的道德脱离、科学不端行为和对算法的负面态度。研究 2 以特质激活理论为基础,调查了来自算法的 NPF 如何引发个人的利己主义和算法厌恶,从而可能导致道德脱离和科学不端行为。结果表明,算法厌恶会引发个人的利己主义,两者相互作用会加强道德脱离,进而导致研究人员的科学不端行为增加。这种关系还受到算法透明度的调节。研究得出结论,虽然算法可以简化绩效评估,但如果设计不当,会给研究人员的科学不端行为带来巨大风险。这些发现扩展了我们对 NPF 的理解,突出了算法在决策过程中面临的情感和认知挑战,同时也强调了平衡技术效率与道德考量的重要性,以促进健康的研究环境。此外,其管理意义还包括在算法 NPF 过程中纳入人工监督,提高透明度和公平性,以减轻对医学研究人员态度和行为的负面影响。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Negative performance feedback from algorithms or humans? effect of medical researchers' algorithm aversion on scientific misconduct.

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.

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来源期刊
BMC Medical Ethics
BMC Medical Ethics MEDICAL ETHICS-
CiteScore
5.20
自引率
7.40%
发文量
108
审稿时长
>12 weeks
期刊介绍: BMC Medical Ethics is an open access journal publishing original peer-reviewed research articles in relation to the ethical aspects of biomedical research and clinical practice, including professional choices and conduct, medical technologies, healthcare systems and health policies.
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