人工智能灵活性检查清单 (ARC):LLM辅助编码的分析前暂停.
1Wee Kim Wee School of Communication and Information, Nanyang Technological University, Singapore, Singapore.
Qualitative health research
|December 24, 2025
概括
本研究介绍了AI-Reflexivity检查清单 (ARC),以指导人工智能辅助的定性健康研究中的人类监督. ARC确保了适当的人在循环 (HITL) 交互,以进行可靠和道德的分析.
科学领域:
- 质量健康研究 质量健康研究
- 人工智能在医学中的应用
- 医疗信息学 医疗信息学
背景情况:
- 人工智能 (AI) 越来越多地被用于定性健康研究,但目前的反射性指导方针不足以解决算法影响.
- 在定性分析中评估人工智能的现有方法是有限的,特别是关于文化细微或情感复杂的数据.
研究的目的:
- 引入AI-Reflexivity检查清单 (ARC),这是一个用于定性健康研究的新型预分析工具.
- 为大型语言模型 (LLM) 辅助的定性编码建立适当的人在循环 (HITL) 姿势.
- 将算法影响评估整合到早期的定性分析中.
主要方法:
- 基于科学和技术研究的文献,人工智能辅助的定性分析和工作流模型的AI-Reflexivity检查清单 (ARC) 的开发.
- 将五个决策领域 (描述范围,上下文变化,体验深度,道德暴露,输出可逆性) 运行化为连续提示.
- 根据任务特征和风险评估,定义HITL姿势 (委托,协助/增强,人为领导).
主要成果:
- 在AI实施定性编码之前,ARC提供了一个结构化的,基于证据的检查点.
- 检查清单指导选择HITL姿势,允许简单任务的自动化,并强制要求复杂或敏感任务的人类监督.
- 通过包括算法行为者和减轻与不分青红白的自动化相关的风险,ARC解决了传统反射能力的局限性.
结论:
- 人工智能反射性检查清单 (ARC) 通过结合算法考虑来增强人工智能辅助的定性健康研究中的反射性.
- ARC促进了对人工智能采用的平衡方法,确保人为监督是根据分析的复杂性和伦理影响量身定制的.
- 该工具为研究人员提供了一种实用的方法来记录和审查人与人工智能的互动,提高了定性健康数据分析的严谨性和可靠性.
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