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Speciesist Language and Nonhuman Animal Bias in English Masked Language Models

Paper · 2022 · Masashi Takeshita, Rafal Rzepka and Kenji Araki

Summary

Research on social bias in NLP has almost always focused on human attributes such as gender, race or religion. This paper asks whether English masked language models also encode bias against nonhuman animals, which the authors call speciesist bias. They connect it to speciesist language from animal ethics: insulting animal terms, euphemisms for killing, and pronoun choices that treat animals as objects (that/which instead of who). The authors probe four pre-trained MLMs on 46 animal names. They use two kinds of input: hand-built templates and real sentences extracted from corpora. They compare what the models predict for masked positions, which is an intrinsic evaluation. The models tend to link harmful or degrading words with many animals. For farm animals they converge on words like "slaughtered", and they prefer object-like relative pronouns for several species. This pronoun preference follows how often each animal is referred to that way in corpora. The work opens a new bias dimension, and it shows that inherited stylistic habits of a language can end up as model bias.

Key points

  • Models probed: BERT LARGE-cased, RoBERTa LARGE, DistilBERT base-cased, ALBERT large-v2. The probe set covers 46 animal names, and the code is released on GitHub (Language-Media-Lab/speciesist-language).

  • Two probe types are used:

    • template sentences, which isolate specific aspects of bias;
    • corpus-extracted sentences, which give a more realistic setting.

  • BERT and RoBERTa group the animal names in the expected way, for example farm animals together. They give the same harmful predictions, such as "slaughtered", to the farm animals.

  • The models prefer that/which over who for many animals. The strength of this preference correlates with corpus frequency of those pronouns per animal.

  • BERT, DistilBERT and ALBERT share a pre-training corpus yet predict different words:

    • BERT produces many profanity-rooted words;
    • DistilBERT and ALBERT give one word to many animals.

    The authors read this as overamplification. RoBERTa, which was trained on more varied corpora, shows more variety.

Interesting ideas

  • I1. The authors trace bias back to a measurable corpus statistic (relative-pronoun frequency per referent) instead of only reporting model outputs, which separates inherited bias from bias the model amplifies.
  • I2. Relative-pronoun choice (who vs which/that) works as a cheap, observable signal of whether a text treats a referent as a person-like agent or as an object.
  • I3. A lexicon mislabel can hide a systematic skew: the paper notes that VADER scores slaughter as neutral, so the word's sentiment silently depends on who the victim is.
  • I4. Probing models that share a pre-training corpus separates corpus effects from architecture effects.
  • I5. Pairing controlled templates with naturally occurring sentences balances precise targeting against realism.

For Mova

How these ideas could serve Mova, our small open language model — its files, stages and tests:

  • I1 (learn): Diagnostics for silver data could compare a parser's or tagger's error profile per lemma with corpus frequency. This would show whether a repair rule fixes a data skew or a model artifact.
  • I2 (world): When building entities, the relativizer and personal pronoun used for a referent could serve as evidence for its animacy or agency status, logged as an explainable feature.
  • I3 (frames): Polarity or harm values on verbs should be attached to the frame together with the patient's class, rather than fixed per lemma. Then slaughter of humans and of animals is handled consistently and transparently.
  • I4 (learn): When several witness parsers are trained on the same treebank, comparing where they disagree can expose model-specific overamplification before votes are combined into silver data.
  • I5 (answer): Reader tests on fables and tales could combine synthetic template questions with questions over real story text. A gap between the two scores would reveal brittleness.

Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.

 Institutions

🎓Hokkaido UniversitySapporo · Japan2 works · 12 citations

📚 Shelves 6 Computer Science · 9,086 works Artificial Intelligence · 8,889 works Arts and Humanities · 9,296 works Language and Linguistics · 9,206 works Social Sciences · 744 works Safety Research · 221 works

🧭 Topics 2 Language models & pretraining · 1,343 works English · 1,373 works

Cite

Cite as Masashi Takeshita, Rafal Rzepka and Kenji Araki. 2022. Speciesist Language and Nonhuman Animal Bias in English Masked Language Models. arXiv:2203.05140.

The paper itself, not this page. BibTeX from the library's record of the work. DOI 10.1016/j.ipm.2022.103050 · arXiv 2203.05140

@misc{takeshita2022speciesist,
  title = {{Speciesist Language and Nonhuman Animal Bias in English Masked Language Models}},
  author = {Masashi Takeshita, Rafal Rzepka and Kenji Araki},
  year = {2022},
  doi = {10.1016/j.ipm.2022.103050},
  eprint = {2203.05140},
  archivePrefix = {arXiv},
  url = {https://doi.org/10.1016/j.ipm.2022.103050}
}

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