评估聊天中的变性GPT响应:模拟在线用户输入的案例研究
Yulin Hswen1,2, Thu Nguyen1,2
1Yulin Hswen, ScD, MPH, is an Assistant Professor, Department of Epidemiology and Biostatistics, University of California San Francisco, San Francisco, California, USA.
概括
生成型人工智能 (AI) 显示出公共卫生承诺,但存在偏见. 这项研究发现,ChatGPT基于种族和性别提供的艾滋病毒建议不那么全面,突出了公平性问题.
科学领域:
- 公共卫生 公共卫生
- 人工智能的人工智能
- 健康 公平 卫生 公平
背景情况:
- 生成型人工智能 (AI) 提供了公共卫生信息获取的潜力.
- 人工智能培训数据中的偏差可能导致不公平或不公平的应用结果.
- 调查人工智能偏见对于确保可靠和有效的公共卫生工具至关重要.
研究的目的:
- 评估社会变量 (种族,性别,性取向) 如何影响生成性AI工具ChatGPT的响应.
- 评估ChatGPT的公共卫生建议中的潜在偏见,特别是关于艾滋病毒.
- 为了比较ChatGPT版本3.5和4.0.0之间的响应差异.
主要方法:
- 用于查询ChatGPT版本3.5和4.0的结构化问题格式.
- 模拟的第一次互动与问题集中在艾滋病毒咨询.
- 对不同的人口输入的综合性和社会决定因素和文化敏感资源的纳入性进行了分析.
主要成果:
- 某些社会变量与ChatGPT的艾滋病毒建议不那么全面有关.
- 两种AI版本都很少提到健康的社会决定因素.
- 文化敏感的资源在人工智能回复中偶尔被引用.
结论:
- 发现了与社会变量相关的人工智能产生的公共卫生建议中的差异.
- 这些发现强调了人工智能系统需要整合多样化的数据源以减轻偏见的必要性.
- 需要进一步的研究,以确保人工智能工具促进健康公平.
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