在研究中使用生成人工智能的未来完整性标准的需要
1University of Michigan, Ann Arbor, MI, United States.
概括
新的生成人工智能 (GenAI) 工具挑战了现有的研究法规. 这篇文章主张在研究中使用GenAI的未来社区标准,以减轻潜在的危害.
科学领域:
- 科学研究法规科学研究法规.
- 医疗研究监督 医疗研究监督
- 科学领域的新兴技术.
背景情况:
- 联邦政府历来平衡研究支持与公共保护.
- 监管框架在临床护理,人类受试者研究和研究完整性之间存在差异.
- 现有的监管模式面临着先进技术带来的新挑战.
研究的目的:
- 在科学研究中解决生成人工智能 (GenAI) 带来的监管挑战.
- 建议对研究中的GenAI进行监管方法的转变.
- 在AI时代,确保研究完整性和公共安全.
主要方法:
- 对科学和医学研究现行监管框架的分析.
- 识别生成人工智能 (GenAI) 工具所带来的挑战.
- 基于社区标准的修订监管战略的论点.
主要成果:
- 生成型人工智能 (GenAI) 工具引入了增加制造,伪造和抄袭的风险.
- 追溯识别GenAI诱导的错误和意图是具有挑战性的.
- 目前不同的监管模式不足以应对GenAI的独特挑战.
结论:
- 建议向研究中使用GenAI的前性,社区定义的标准迈进.
- 通过主动制定明确的准则,可以更好地管理与GenAI相关的风险.
- 这种方法旨在保持研究的完整性和公众的信任.
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