对在线研究数据有效性的新威胁的规划:使用计算机代理人的问题
Jon Agley1,2
1Prevention Insights, School of Public Health-Bloomington, Indiana University Bloomington, IN, USA.
Evaluation & the health professions
|August 7, 2025
概括
大型语言模型 (LLM) 可能威胁到通过众包平台收集的在线研究数据的有效性. 需要进一步讨论,以解决这些对研究诚信的新威胁.
科学领域:
- 计算机科学 计算机科学
- 社会科学 社会科学 社会科学
- 研究方法研究方法研究方法学
背景情况:
- 在线研究越来越多地利用众包平台,如亚马逊的Mechanical Turk (MTurk) 和Prolific进行参与者招聘.
- 对众包数据有效性的确定的威胁包括机器人,注意力不足和参与者误导.
- 开发了质量控制技术,以确保这些平台上的可信研究.
研究的目的:
- 探索大语言模型 (LLM) 对众包研究数据有效性的潜在新威胁.
- 检查先进的人工智能,特别是计算机使用代理 (CUA) 如何影响未来的众包数据收集.
- 鼓励对话和进一步研究在线研究中减轻人工智能驱动的有效性威胁.
主要方法:
- 这份简短的报告主要是理论和探索性的.
- 它分析了当前和新兴的LLM的功能,例如OpenAI的"运营商".
- 它讨论了这些人工智能进步对众包研究完整性的影响.
主要成果:
- 大型语言模型,特别是使用计算机的代理商,代表了对众包研究有效性的潜在威胁的新类别.
- 这些人工智能代理可以模仿人类参与者,引入复杂的不注意或误导形式.
- 现有的质量控制措施可能不足以应对先进的人工智能驱动的数据收集.
结论:
- 复杂的LLM的兴起需要重新评估众包研究中的有效性威胁.
- 积极研究和开发新的质量控制策略对于维护数据完整性至关重要.
- 进一步讨论和跨学科合作对于应对这些新出现的挑战至关重要.
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