在人工智能语言模型中对神经分歧相关术语的偏见的普遍性
Sam Brandsen1,2, Tara Chandrasekhar1,2, Lauren Franz1,2
1Department of Psychiatry and Behavioral Sciences, Duke University, Durham, North Carolina, USA.
概括
人工智能 (AI) 模型显示了对神经分歧术语的重大偏见,将它们与负面概念联系在一起. 这种偏见甚至在检查优势时也存在,这表明AI语言处理中存在广泛的问题.
科学领域:
- 神经科学是一个神经科学.
- 人工智能的人工智能
- 心理学 心理学 心理学
背景情况:
- 人工智能 (AI) 越来越多地被用于决策.
- 人们对人工智能语言模型中的潜在偏见存在担忧.
- 神经分离性疾病经常被误解和污名化.
研究的目的:
- 调查人工智能对与神经分离条件相关的术语的偏见.
- 量化神经多样性术语与负面概念之间的关联.
- 探索与神经分离个体的挑战和优势相关的偏见.
主要方法:
- 使用了11个语言模型编码器来分析单词关联.
- 使用词嵌入协会测试 (WEAT) 来测量偏差.
- 引入了句子相似性比率测试,用于直接的短语分析.
主要成果:
- 在经过测试的神经分歧术语中发现了持续高水平的AI偏差.
- 在神经多样性术语和危险和疾病等概念之间观察到负面关联.
- 偏见甚至在与神经分歧强项相关的术语中也很明显,例如自闭症中的诚实.
- 描述残疾的句子显示出比控制句子更强烈的负面关联.
结论:
- 当前的人工智能语言模型对神经分离个体有显著的偏见.
- 这种偏见可以延续有害的刻板印象和误解.
- 进一步的研究和缓解策略对于公平的人工智能发展至关重要.
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