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Sociodemographic Bias in Language Models: A Survey and Forward Path

Paper · 2023 · Vipul Gupta, Pranav Narayanan Venkit, Shomir Wilson et al.

Summary

Language models tend to absorb social prejudices from their training corpora. When they behave unequally across demographic groups, they can cause real harm once deployed. Gupta et al. review roughly ten years of research on this sociodemographic bias. They ran keyword searches in the ACL Anthology, NeurIPS, FAccT and AIES and followed citation links. They then filtered 308 candidates down to 273 text-focused papers published before 2024. These are sorted into a three-part typology: which kinds of bias are studied, how bias is measured, and how it is reduced. The authors also trace how measurement and mitigation techniques changed over time. They ground the discussion in psychology, for example Kahneman's view that some biases are useful mental shortcuts. Their analysis shows the field is skewed toward gender and English, and that many metrics are fragile. They close with a 13-question checklist and argue that mitigating bias during training, for instance with expert models, is the most promising route. The work gives newcomers a map of the area and a practical tool for positioning new studies.

Key points

  • The corpus contains 273 papers (narrowed from 308). Speech research was excluded. The typology has three branches: types of bias (T), quantifying bias (Q) and debiasing (D).
  • Gender bias dominates the corpus with 114 papers (48%). It is followed by race (36, 15%), ethnicity (24, 10%) and nationality (18, 7%). Socioeconomic status gets only 4 papers.
  • Over 90% of the reviewed works study English only. Languages like German, Korean, Turkish, Chinese and Hindi get little attention, a gap that also matters for low-coverage languages such as Ukrainian.
  • Early measurement (2013–2019) relied on distances in embedding space, using WEAT (inspired by the Implicit Association Test), ECT and RIPA. Template- and prompt-based methods came later, and template-based ones suffer from robustness problems.
  • The checklist has 13 questions: 6 on bias measurement, 2 on mitigation and 5 general ones (e.g., reproducibility, interdisciplinarity). The authors expect future work to focus on training-time debiasing (contrastive learning, expert models) and intersectional bias.

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

🎓Pennsylvania State UniversityState College · United States56 works · 5,360 citations

 Fields, topics and concepts

📚 Shelves 6The field of science this work belongs to (OpenAlex fields).Computer Science · 16,667 worksArtificial Intelligence · 16,194 worksArts and Humanities · 16,039 worksLanguage and Linguistics · 15,891 worksSocial Sciences · 1,772 worksSafety Research · 543 works

🧭 Topics 1Research subjects and languages the work is about.Language models & pretraining · 3,320 works

Cite

Cite as Vipul Gupta, Pranav Narayanan Venkit, Shomir Wilson and Rebecca J. Passonneau. 2023. Sociodemographic Bias in Language Models: A Survey and Forward Path. arXiv:2306.08158.

The paper itself, not this page. BibTeX from the library's record of the work. DOI 10.48550/arxiv.2306.08158 · arXiv 2306.08158

@misc{gupta2023sociodemographic,
  title = {{Sociodemographic Bias in Language Models: A Survey and Forward Path}},
  author = {Vipul Gupta, Pranav Narayanan Venkit, Shomir Wilson and Rebecca J. Passonneau},
  year = {2023},
  doi = {10.48550/arxiv.2306.08158},
  eprint = {2306.08158},
  archivePrefix = {arXiv},
  url = {https://doi.org/10.48550/arxiv.2306.08158}
}

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