生成型人工智能中性别和种族偏见 药剂师的文字到图像描述
Geoffrey Currie1,2, George John1, Johnathan Hewis3
1School of Dentistry and Medical Sciences, Charles Sturt University, Wagga Wagga, Australia.
The International journal of pharmacy practice
|September 4, 2024
概括
使用DALL-E 3的生成人工智能图像生成显示了显著的性别和种族偏见,不成比例地将澳大利亚药剂师描绘成白人男性. 这种人工智能偏见并不反映澳大利亚药房专业的实际多样性.
科学领域:
- 人工智能的人工智能
- 卫生专业 卫生专业 卫生专业
- 社会学 社会学 社会学
背景情况:
- 澳大利亚的药剂师主要是女性 (64%),但代表性仍然不足.
- 生成型人工智能虽然具有变革性,但存在错误,误解和偏见的风险.
- 像DALL-E 3这样的生成人工智能文本到图像工具可能会延续性别和种族刻板印象.
研究的目的:
- 为了评估DALL-E 3中的性别和种族偏见,DALL-E 3生成了澳大利亚药剂师的图像.
- 将人工智能生成的图像与澳大利亚药房劳动力的人口统计现实进行比较.
主要方法:
- 2024年3月,DALL-E 3被用于创建澳大利亚药剂师的40张图像 (30个人,10组).
- 两个独立的评论员分析了图像的性别,年龄,种族,肤色和身体习惯.
- 通过第三名观察员,就任何差异达成共识.
主要成果:
- 通过DALL-E 3生成的图像显示了69.7%的男性,29.7%的女性,93.5%的皮肤肤色浅,6.5%的皮肤肤色中,0%的皮肤肤色深.
- 这种性别分布与实际的澳大利亚药剂师有很大的不同 (P < .001).
- 个别药剂师的图像完全是男性 (100%) 和肤浅 (100%).
结论:
- 生成型人工智能文本到图像生成 (DALL-E 3) 显示出显著的性别和种族偏见.
- 人工智能生成的图像过度代表白人作为药剂师,无法反映澳大利亚药房专业的多样性.
- 这种偏见凸显了在专业环境中对人工智能工具的批判性评估的必要性.
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