超越刻板印象:人工智能图像生成和麻醉学中的多样性
Mia Gisselbaek1, Laurens Minsart2, Ekin Köselerli3
1Division of Anesthesiology, Department of Anesthesiology, Clinical Pharmacology, Intensive Care and Emergency Medicine, Faculty of Medicine, Geneva University Hospitals, Geneva, Switzerland.
Frontiers in artificial intelligence
|October 24, 2024
概括
人工智能图像生成器显示显著的种族和性别偏见,未能代表麻醉学工作人员的多样性. 这凸显了需要改进人工智能训练数据的必要性,以确保准确的医学图像.
科学领域:
- 医疗信息学 医疗信息学
- 人工智能在医学中的应用
- 医学教育 医学教育
背景情况:
- 人工智能 (AI) 越来越多地用于麻醉学,以提高患者的安全性和效率.
- 了解AI对专业代表性的影响至关重要.
研究的目的:
- 为了评估人工智能生成的图像在麻醉学中的人口统计准确性.
- 在人工智能描绘麻醉学工作人员时识别社会偏见.
主要方法:
- 人工智能生成图像的横截面分析 (2024年1月至2月).
- 美国麻醉学会 (ASA) 和欧洲麻醉学和重症监护学会 (ESAIC) 的人口统计数据.
- 独立审稿人对来自ChatGPT DALL-E 2和Midjourney的1200张图像进行了性别,种族/种族,年龄和情感特征的评估.
主要成果:
- 与ASA/ESAIC数据相比,人工智能模型显著低于代表人口多样性.
- 主要将麻醉师描绘成白人 (64.2%-83.0%).
- 在性别和种族/种族之间存在偏见的关联,年轻专业人士的代表性不足.
结论:
- 人工智能模型在性别,种族/种族和年龄表现方面表现出明显的偏见.
- 当前的人工智能图像并不反映麻醉师劳动力的实际多样性.
- 在AI中需要多样化的培训数据集和偏差缓解策略,以获得准确的医学代表性.
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