揭示NLP研究中对非人动物的系统性忽视与偏见
Speciesism in Natural Language Processing Research
- 调查学者、数据集和模型中的物种主义现象
- 发现研究者普遍不识别物种偏见，数据与GPT模型存在固有偏见
- 呼吁将动物伦理纳入AI安全与偏见研究框架
自然语言处理（NLP）研究长期聚焦人类安全与社会偏见，却忽视了非人动物的道德地位。本研究旨在考察NLP领域是否存在针对非人动物的物种主义（speciesism）。首先阐明非人动物在NLP中的相关性，继而通过调研与实验分析现有研究，发现物种主义存在于研究者、数据与模型中：（a）即使关注社会偏见的NLP研究者也未意识到物种主义；（b）用于评估模型的数据集中存在固有物种偏见；（c）OpenAI GPT系列模型默认表现出物种主义倾向。研究最后讨论如何在NLP研究中减少物种主义。
原文摘要 · Abstract (English)
Natural Language Processing (NLP) research on AI Safety and social bias in AI has focused on safety for humans and social bias against human minorities. However, some AI ethicists have argued that the moral significance of nonhuman animals has been ignored in AI research. Therefore, the purpose of this study is to investigate whether there is speciesism, i.e., discrimination against nonhuman animals, in NLP research. First, we explain why nonhuman animals are relevant in NLP research. Next, we survey the findings of existing research on speciesism in NLP researchers, data, and models and further investigate this problem in this study. The findings of this study suggest that speciesism exists within researchers, data, and models, respectively. Specifically, our survey and experiments show that (a) among NLP researchers, even those who study social bias in AI, do not recognize speciesism or speciesist bias; (b) among NLP data, speciesist bias is inherent in the data annotated in the datasets used to evaluate NLP models; (c) OpenAI GPTs, recent NLP models, exhibit speciesist bias by default. Finally, we discuss how we can reduce speciesism in NLP research.
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