人工智能,原创性和归因:研究人员关于区分贡献的观点
Yanyi Wu1,2, Xinyu Lu1,2, Chenghua Lin1,2
1School of Public Affairs, Zhejiang University, Hangzhou, China.
Accountability in research
|July 20, 2025
概括
研究人员将人工智能 (AI) 视为一种工具,而不是合作者,使用主观方法来归因工作. 这凸显了需要明确的指导方针来维持人工智能辅助研究的研究完整性.
科学领域:
- 学术研究实践学术研究实践.
- 人工智能中的伦理学
- 人与计算机的互动.
背景情况:
- 人工智能 (AI) 整合挑战了关于原创性,贡献和作者身份的传统学术规范.
- 研究中人工智能的现有政策正在发展,但缺乏了解研究人员对这些模两可的主观导航.
- 这种差距影响了涉及人工智能的研究的完整性.
研究的目的:
- 探索研究人员如何在他们的工作中感知和区分人类和人工智能的贡献.
- 了解研究人员在人工智能辅助研究中使用的实践策略,以克服归因模糊性.
- 确定人工智能集成带来的伦理紧张和需要更清晰的指导方针.
主要方法:
- 与18名STEM,社会科学和人文学科的研究人员进行了半结构化采访.
- 包括不同职业阶段的参与者:博士生,博士后研究人员和教师.
- 通过反射主题分析分析了采访数据,以归因理论为指导.
主要成果:
- 研究人员主要将人工智能 (AI) 视为一种复杂的工具,而不是一个独立的合作者.
- 主观启发式,如"直觉"和基于努力的归因,用于管理归因模两可.
- 显著的伦理紧张局势出现,加上对更清晰,更细致的指导方针的强烈愿望.
结论:
- 研究人员在将传统的完整性规范应用于人工智能辅助的工作时遇到认知和实际挑战.
- 调查结果强调了需要进行批判性对话和反思性的做法.
- 细微的准则对于维护研究完整性和将人类价值与人工智能能力相结合至关重要.
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