相关实验视频
从混沌到共生:探索用于生成人工智能和研究完整性系统的适应性共同进化策略
Wenqing Miao1, Huan Zang1, Qirui Liu2,3
1Department of Science and Technology, The Affiliated Hospital of Xuzhou Medical University, Xuzhou, Jiangsu, 221006, People's Republic of China.
BMC medical ethics
|September 27, 2025
概括
在研究中采用生成人工智能 (GenAI) 引入了完整性风险. 复杂自适应系统 (CAS) 理论揭示了研究人员,管理人员和出版商如何适应,需要新的科学诚信治理.
科学领域:
- 科学完整性管理科学完整性管理
- 研究中的新兴技术.
- 复杂自适应系统 (CAS) 理论
背景情况:
- 生成型人工智能 (GenAI) 越来越多地融入科学研究.
- 基因人工智能 (GenAI) 的采用对科学研究的完整性提出了新的挑战.
- 现有的完整性管理系统需要调整以应对与GenAI相关的风险.
研究的目的:
- 在GenAI采用期间检查科学研究完整性的风险因素和预防措施.
- 应用复杂自适应系统 (CAS) 理论来分析研究完整性管理系统.
- 为管理GenAI集成研究中的诚信提供新的视角.
主要方法:
- 复杂自适应系统 (CAS) 理论的应用.
- 分析科学研究完整性管理系统作为一个CAS.
- 确定四个核心微层代理人:研究人员,研究对象,科学研究管理人员和学术出版机构.
- 检查宏观系统的复杂性,代理的适应性和代理的相互作用.
主要成果:
- 研究完整性管理系统的特点是结构,层次和多维的复杂性.
- 基因人工智能集成促使研究人员,研究对象,管理人员和出版机构的适应性行为变化.
- 代理人之间的互动导致宏观层面的结果出现,推动完整性管理系统的演变.
结论:
- 科学不当行为的风险迫切需要治理,特别是与GenAI的整合.
- 建议包括定义GenAI的界限,建立监管框架,加强审查/披露协议.
- 将完整性考虑纳入GenAI的开发和改进对于未来的研究生态系统至关重要.
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