人工智能会根据人们的方言做出隐蔽的种族主义决定.
Valentin Hofmann1,2,3, Pratyusha Ria Kalluri4, Dan Jurafsky4
1Allen Institute for AI, Seattle, WA, USA. valentinh@allenai.org.
Nature
|August 28, 2024
概括
语言模型表现出隐蔽的种族主义, 显示非洲裔美国人比人类更强烈的负面刻板印象. 目前的偏见减轻技术加剧了这种隐藏的种族偏见.
科学领域:
- 人工智能
- 社会语言学
- 计算机科学
背景情况:
- 语言模型 (LMs) 被广泛使用,
- 之前的研究集中在公开的种族主义上,
- 隐蔽的种族主义,特别是民权后的种族主义,是社会科学研究的一个发展领域.
研究的目的:
- 调查隐藏的种族主义,特别是方言偏见是否表现在大型语言模型中.
- 为了比较LM对非洲裔美国人和非洲裔美国人英语 (AAE) 的隐蔽和公开刻板印象.
- 评估当前偏见缓解策略对这些刻板印象的影响.
主要方法:
- 在大型语言模型中嵌入的刻板印象分析.
- 由LM产生的刻板印象与实验记录的人类刻板印象的比较.
- 评估偏差缓解技术,如人类偏好对齐.
主要成果:
- 对于非洲裔美国人英语 (AAE) 的人来说,LM表现出明显的方言偏见.
- 对于AAE说话者的LM刻板印象比人类刻板印象更为负面.
- 对于非裔美国人来说,
- 人类偏好调整等偏见缓解方法扩大了隐蔽和公开刻板印象之间的差异.
结论:
- 语言模式通过方言偏见体现了隐蔽的种族主义,
- 目前的偏见缓解做法是不够的,
- 为了公平和安全的人工智能应用,需要对LM的开发和部署进行紧急重新评估.
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