Summary of Speciesism in Natural Language Processing Research, by Masashi Takeshita and Rafal Rzepka
Speciesism in Natural Language Processing Research
by Masashi Takeshita, Rafal Rzepka
First submitted to arxiv on: 18 Oct 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper investigates speciesism, or discrimination against nonhuman animals, in Natural Language Processing (NLP) research. It argues that while AI ethicists have focused on safety for humans and social bias against human minorities, the moral significance of nonhuman animals has been ignored. The study surveys existing research and conducts experiments to show that speciesism exists among NLP researchers, data, and models. Specifically, it finds that even researchers who study social bias in AI do not recognize speciesism or speciesist bias, speciesist bias is inherent in annotated datasets used to evaluate NLP models, and OpenAI GPTs exhibit speciesist bias by default. The paper concludes by discussing how to reduce speciesism in NLP research. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper looks at a big problem in AI called speciesism, where we’re unfairly treating animals worse than humans just because they’re not human. Right now, most AI research is focused on making sure AI doesn’t hurt people and that it’s fair to everyone – but what about animals? The researchers are saying that even though many AI experts know that bias can be a problem for certain groups of people, they don’t realize that the same thing happens to animals. They found that some AI models have built-in biases against animals just because they’re not human. This is important because it means we need to think about how our AI systems are treating animals and make sure they’re fair and kind. |
Keywords
» Artificial intelligence » Natural language processing » Nlp
