Intrinsic bias metrics do not correlate with application bias
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Seraphina Goldfarb-Tarrant, Rebecca Marchant, Ricardo Muñoz Sánchez, Mugdha Pandya, Adam Lopez. Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers). 2021.
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Goldfarb-Tarrant et al. (2021) find no reliable correlation at all between embedding bias and downstream bias.
“In fact, Goldfarb-Tarrant et al. (2021) find no reliable correlation at all”
From Bias and Fairness in Large Language Models: A Survey (2024) · cites this paper as Goldfarb-Tarrant et al. 2021
The Discussion (section 6) reports no reliable overall relationship between changes in WEAT and changes in application bias. But the Conclusion (section 7) says positive correlations exist in restricted settings, so “no reliable correlation at all” is too absolute. (LLM, conf=0.88)
Experimental — accuracy not yet measuredBiases in the embedding space have only weak or inconsistent relationships with biases in downstream tasks.
“Several works point out that biases in the embedding space have only weak or inconsistent relationships with biases in downstream tasks (Cabello, Jørgensen, and Søgaard 2023; Cao et al. 2022a; Goldfarb-Tarrant et al. 2021; Orgad and Belinkov 2022; Orgad, Goldfarb-Tarrant, and Belinkov 2022; Steed et al. 2022).”
From Bias and Fairness in Large Language Models: A Survey (2024) · cites this paper as Goldfarb-Tarrant et al. 2021
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- Authors
- Goldfarb-Tarrant, S., Marchant, R., Muñoz Sánchez, R., Pandya, M., & Lopez, A.
- Affiliations
- University of Washington (US)
- University of Edinburgh (GB)
- Venue
- Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)
- Published
- 2021
- Pages
- 1926-1940
- OpenAlex
- W3121085131
- Cited by
- 33
- References
- 40
- Citations by year
- 2026: 6 · 2025: 5 · 2024: 5 · 2023: 10 · 2022: 4 · 2021: 3
- FWCI
- 4.74
- Type
- conference paper
- Language
- en
- Open access
- gold
- doi.org
- Licence
- cc-by
- Locations
- arXiv (Cornell University) (submittedVersion)
- Edinburgh Research Explorer (University of Edinburgh) (submittedVersion, cc-by)
- arXiv (Cornell University) (cc-by)
- Funders
- Engineering and Physical Sciences Research Council (EP/T023767/1)
- Topics
- Social Sciences › Safety Research › Ethics and Social Impacts of AI
- Physical Sciences › Computer Science › Artificial Intelligence › Natural Language Processing Techniques
- Physical Sciences › Computer Science › Artificial Intelligence › Topic Modeling
- Keywords
- algorithmic fairness, bias measurement, algorithmic discrimination
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