公平的框架:伦理混合的同行评审
Amos Grünebaum1, Joachim Dudenhausen2, Frank A Chervenak1
15799 Northwell Health, Zucker School of Medicine , Hempstead, NY, USA.
Journal of perinatal medicine
|September 22, 2025
概括
该FAIR框架结合了人工智能和人类专业知识,以创建一个更有效和公平的同行评审系统. 这种创新方法解决了偏见和延迟,提高了科学出版的质量.
科学领域:
- 科学出版科学出版
- 学术研究学术研究
- 学术传播学术交流
背景情况:
- 传统的同行评审存在偏见,延误和缺乏透明度.
- 这些问题阻碍了科学进步,并带来了巨大的年度成本.
- 目前的系统未能在学术劳动中维护道德义务.
研究的目的:
- 提出一个以道德为导向的混合同行评审系统.
- 将生成性人工智能 (AI) 与人类专业知识相结合.
- 解决当前审查流程的根本缺陷.
主要方法:
- 开发了FAIR框架 (公平,问责,诚信和责任).
- 分析同行评审失败和集成的人工智能能力.
- 雇佣了标准化的快速工程与人类监督.
主要成果:
- 公平地通过算法检测和标准化协议解决偏见.
- 通过透明的审计轨道和记录的决策,确保问责制.
- 通过安全的本地人工智能处理和保密保障来保持完整性.
结论:
- 公平的框架为同行评审效率低下提供了一个原则性的解决方案.
- 结合了人工智能支持的一致性和速度与基本的人类专业知识.
- 减少审查延迟,消除偏见,提高透明度.
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