算法或人类的负面绩效反? 医学研究人员对算法厌恶对科学不当行为的影响
Ganli Liao1, Feiwen Wang2, Wenhui Zhu3
1Business School, Beijing Information Science and Technology University, Beijing, China. glliao@bistu.edu.cn.
BMC medical ethics
|October 24, 2024
概括
与人类反相比,算法带来的负效率反 (NPF) 增加了医学研究人员的道德脱离和科学不当行为. 算法透明度和人类监督对于减轻这些风险至关重要.
科学领域:
- 行为科学 行为科学
- 组织心理学 组织心理学
- 科学伦理学 科学伦理学
背景情况:
- 机构越来越多地使用算法进行绩效反,取代人类经理.
- 这种转变引发了对算法反的有效性和公平性的担忧.
研究的目的:
- 调查算法与人类对医学研究人员态度,认知和行为的负面绩效反 (NPF) 的影响.
- 根据反来源,比较科学不当行为,道德脱,以及对算法的态度.
主要方法:
- 两个基于场景的实验研究,涉及660名医学研究人员.
- 研究1:比较了算法与人类在不当行为,道德脱离和算法态度方面的NPF.
- 研究2:研究了算法中的NPF如何触发自私和算法厌恶,通过透明度调节,使用特征激活理论.
主要成果:
- 来自算法的NPF导致了比人类的NPF更高的道德脱离,科学不当行为和负面的算法态度.
- 算法厌恶引发了自私,它们的相互作用放大了道德脱离,增加了科学不当行为.
- 算法透明度调节了NPF与不当行为之间的关系.
结论:
- 虽然算法在绩效评估中提供了效率,但如果不仔细设计,它们会增加科学不当行为的风险.
- 解决算法决策的情感和认知挑战至关重要.
- 技术效率与道德考虑,人类监督和透明度之间的平衡对于健康的研究环境至关重要.
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