检测大模型对同性及跨种族恋情的偏见，发现对亚裔名字更不敏感
On the Influence of Gender and Race in Romantic Relationship Prediction from Large Language Models
- 通过替换名字测试模型对恋情预测的性别与种族偏好
- 同性配对、亚裔姓名组合被预测为情侣的概率更低
- 适合关注AI公平性与社会影响的研究者阅读
我们通过受控的名字替换实验，研究大语言模型在恋爱关系预测任务中是否存在异性恋偏见和对跨种族恋情的歧视。结果表明，模型预测同性角色配对为恋爱关系的概率低于异性配对；同时，涉及亚裔姓名的同族或跨族配对，其被预测为恋爱关系的概率也显著低于黑人、西班牙裔或白人姓名。我们分析了姓名的上下文嵌入，发现亚裔姓名的性别特征在模型中更难辨识。这些发现揭示了模型中的社会偏见，强调需推动更具包容性和公平性的技术发展。
原文摘要 · Abstract (English)
We study the presence of heteronormative biases and prejudice against interracial romantic relationships in large language models by performing controlled name-replacement experiments for the task of relationship prediction. We show that models are less likely to predict romantic relationships for (a) same-gender character pairs than different-gender pairs; and (b) intra/inter-racial character pairs involving Asian names as compared to Black, Hispanic, or White names. We examine the contextualized embeddings of first names and find that gender for Asian names is less discernible than non-Asian names. We discuss the social implications of our findings, underlining the need to prioritize the development of inclusive and equitable technology.
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