In this paper, we trace the biases in current natural language processing (NLP) models back to their origins in racism, sexism, and homophobia over the last 500 years. We review literature from critical race theory, gender studies, data ethics, and digital humanities studies, and summarize the origins of bias in NLP models from these social science perspective. We show how the causes of the biases in the NLP pipeline are rooted in social issues. Finally, we argue that the only way to fix the bias and unfairness in NLP is by addressing the social problems that caused them in the first place and by incorporating social sciences and social scientists in efforts to mitigate bias in NLP models. We provide actionable recommendations for the NLP research community to do so.

翻译：本文追溯了当前自然语言处理（NLP）模型中偏见的起源，这些偏见根植于过去500年间的种族主义、性别歧视与同性恋恐惧症。我们系统梳理了批判种族理论、性别研究、数据伦理及数字人文研究领域的文献，从社会科学视角归纳了NLP模型中偏见的起源。通过分析，我们揭示了NLP流水线中各类偏见的成因如何植根于社会问题。最终，我们主张解决NLP中偏见与不公平问题的根本途径在于：首先直面造成这些偏见的社会问题根源，并将社会科学及相关学者纳入缓解NLP模型偏见的实践中。为此，我们向NLP研究社区提出了具有可操作性的建议。