Social biases can manifest in language agency. For instance, White individuals and men are often described as "agentic" and achievement-oriented, whereas Black individuals and women are frequently described as "communal" and as assisting roles. This study establishes agency as an important aspect of studying social biases in both human-written and Large Language Model (LLM)-generated texts. To accurately measure "language agency" at sentence level, we propose a Language Agency Classification dataset to train reliable agency classifiers. We then use an agency classifier to reveal notable language agency biases in 6 datasets of human- or LLM-written texts, including biographies, professor reviews, and reference letters. While most prior NLP research on agency biases focused on single dimensions, we comprehensively explore language agency biases in gender, race, and intersectional identities. We observe that (1) language agency biases in human-written texts align with real-world social observations; (2) LLM-generated texts demonstrate remarkably higher levels of language agency bias than human-written texts; and (3) critical biases in language agency target people of minority groups--for instance, languages used to describe Black females exhibit the lowest level of agency across datasets. Our findings reveal intricate social biases in human- and LLM-written texts through the lens of language agency, warning against using LLM generations in social contexts without scrutiny.

翻译：社会偏见可能通过语言能动性表现出来。例如，白人个体和男性常被描述为"具有能动性"和以成就为导向，而黑人个体和女性则常被描述为"社群导向"和辅助性角色。本研究将能动性确立为研究人类撰写文本和大语言模型生成文本中社会偏见的重要维度。为在句子层面准确测量"语言能动性"，我们提出一个语言能动性分类数据集，用于训练可靠的能动性分类器。随后，我们利用该分类器揭示了6个人类或大语言模型撰写文本数据集（包括传记、教授评语和推荐信）中显著的语言能动性偏见。尽管此前大多数关于能动性偏见的自然语言处理研究仅关注单一维度，我们全面探索了性别、种族及交叉身份中的语言能动性偏见。研究发现：（1）人类撰写文本中的语言能动性偏见与现实社会观察一致；（2）大语言模型生成文本中的语言能动性偏见显著高于人类撰写文本；（3）语言能动性中存在的关键偏见针对少数群体——例如，描述黑人女性时所使用的语言在所有数据集中表现出最低水平的能动性。本研究通过语言能动性视角揭示了人类与大语言模型撰写文本中错综复杂的社会偏见，警示在未加审视的社会场景中使用大语言模型生成内容的潜在风险。