人工智能产生的面孔影响了性别刻板印象和种族同质化.
Nouar AlDahoul1, Talal Rahwan2, Yasir Zaki3
1New York University, Abu Dhabi, UAE.
Scientific reports
|April 25, 2025
概括
稳定扩散人工智能表现出种族和性别刻板印象,使不同群体的描述均化. 消除偏见的方法和包容性AI图像可以减少人类的偏见.
科学领域:
- 人工智能的人工智能
- 计算机视觉 计算机视觉
- 社会心理学 社会心理学
背景情况:
- 像稳定扩散这样的文本到图像生成AI模型被广泛使用.
- 这些模型中种族和性别刻板印象的存在和程度尚未完全理解.
- 人工智能产生的图像可能会延续社会偏见.
研究的目的:
- 在稳定扩散中记录种族和性别偏见.
- 调查AI图像生成中的种族同质化程度.
- 提出和评估脱皮解决方案及其对人类偏见的影响.
主要方法:
- 稳定扩散分析结果跨越了六个种族,两个性别,32个职业和八个属性.
- 在生成的图像中检查种族相似性.
- 开发和测试允许用户指定的人口分布的 debiasing 技术.
- 一个预先注册的调查实验,评估包容性与非包容性AI面对人类偏见的影响.
主要成果:
- 在稳定扩散中记录了显著的种族和性别刻板印象.
- 观察到大量的种族同质化,特定群体被刻板描绘 (例如,中东男性).
- 拟议的退化解决方案显示了用户对人口分布的控制潜力.
- 接触包容性AI面孔减少了人类偏见,而非包容性面孔增加了它们,无论AI标签如何.
结论:
- 像稳定扩散这样的文本到图像人工智能模型表现出显著的偏见和刻板印象.
- 人种同质化是当前人工智能图像生成的一个关键问题.
- 消除偏见的策略和使用包容性AI产生的内容可以减轻人工智能驱动和人类偏见.
- 解决人工智能的偏见对于负责任的技术开发和部署至关重要.
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