评估和减轻基于AI的医学文本生成中的偏见
Xiuying Chen1,2, Tairan Wang3, Juexiao Zhou3
1Mohamed bin Zayed University of Artificial Intelligence, Abu Dhabi, United Arab Emirates. xiuying.chen@mbzuai.ac.ae.
Nature computational science
|April 24, 2025
概括
医学中的人工智能 (AI) 显示了服务不足群体的绩效差距. 一个新的算法可以提高AI文本生成的公平性,而不会降低整体性能.
科学领域:
- 医疗人工智能的人工智能
- 自然语言处理自然语言处理.
- 算法的公平性算法公平性
背景情况:
- 深度学习人工智能实现了医学领域的专家表现.
- 人们担心人工智能会放大人类偏见,特别是在服务不足的人群中.
- 医学AI中的公平性在成像方面受到研究,但在文本生成方面不那么受到研究.
研究的目的:
- 调查医疗AI文本生成中的公平性问题.
- 识别人口群体之间的绩效差异.
- 开发和评估一个偏差缓解算法.
主要方法:
- 分析了跨种族,性别和年龄组的人工智能文本生成的性能差异.
- 开发了一种用于选择性优化服务不足群体的新算法.
- 在多个AI骨干,数据集和模式上评估了算法.
主要成果:
- 在跨人口群体的医疗AI文本生成中观察到显著的性能差异.
- 拟议的算法有效地减少了文本生成中的偏差.
- 在不影响整体AI性能的情况下,实现了公平性改进.
结论:
- 医疗AI文本生成系统表现出公平性问题.
- 提出的选择性优化算法成功地减轻了偏差.
- 这种方法增强了对不同人群的医学AI公平性.
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