Speciesism in Natural Language Processing Research

TL;DR

This paper investigates the presence of speciesism in NLP research, revealing biases among researchers, data, and models, and discusses strategies to mitigate this form of discrimination.

Contribution

It is the first comprehensive study to identify and analyze speciesism in NLP research, highlighting biases in data, models, and researcher awareness.

Findings

Researchers often do not recognize speciesist bias.

Speciesist bias is present in NLP datasets.

Recent NLP models exhibit speciesist bias by default.

Abstract

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.