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Stereotyping Norwegian Salmon: An Inventory of Pitfalls in Fairness Benchmark Datasets
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Su Lin Blodgett, Gilsinia Lopez, Alexandra Olteanu, Robert Sim, Hanna Wallach. 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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“Evaluating the expanding capabilities of LLMs is a complex research area28,29, with group-specific bias benchmarks shown to be time-consuming to develop and utilize30–33, and the overall field lacking measurement validity and theoretical grounding30,34.”
From Generative language models exhibit social identity biases (2024) · cites this paper as [34]
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
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“Many approaches, for instance, assume some implicitly desirable criterion ( e.g. , a model output should be independent of any social group in the input), but do not explicitly acknowledge or state the normative social values that justify their framework.”
From Bias and Fairness in Large Language Models: A Survey (2024) · cites this paper as Blodgett et al. 2021
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“In nearly half of all instances, these datasets contain ambiguities about what stereotypes they capture, raising questions whether they are valid indicators of real-world stereotypes at all.”
From Bias and Fairness in Large Language Models: A Survey (2024) · cites this paper as Blodgett et al. (2021)
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“several widely-used evaluation datasets suffer from reliability and validity issues, including ambiguities about whether instances accurately reflect real-world stereotypes, inconsistent treatment of social groups, assumptions of near-perfect understanding of language, and lack of syntactic and semantic diversity (Blodgett et al. 2021; Gupta et al. 2023; Selvam et al. 2023).”
From Bias and Fairness in Large Language Models: A Survey (2024) · cites this paper as Blodgett et al. 2021
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“Blodgett et al. (2021) highlight several severe shortcomings of several counterfactual input datasets, namely Winogender, WinoBias, StereoSet, and CrowS-Pairs.”
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“At the core of these issues are (1) unclear articulation of the power imbalances, harm, or other stereotype content present in each instance; and (2) inconsistent, invalid, or unrelated perturbations of social groups.”
From Bias and Fairness in Large Language Models: A Survey (2024) · cites this paper as Blodgett et al. (2021)
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“These datasets also suffer from some data reliability issues, but to a lesser extent than those discussed in Blodgett et al. (2021) (Liang et al. 2022).”
From Bias and Fairness in Large Language Models: A Survey (2024) · cites this paper as Blodgett et al. (2021)
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“In addition, prompt-based learning with GPT-3 has achieved very strong performances in zero-shot and one-shot settings (i.e. with only zero or one labelled example), without any fine-tuning [7].”
From GPT-3-Driven Pedagogical Agents to Train Children’s Curious Question-Asking Skills (2024) · cites this paper as [7]
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Cites
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“Yet even as such benchmarks are added to popular NLP leaderboards like Super-Glue (Wang et al., 2019), whether these they actually help measure the extent to which NLP systems produce computational harms remains unknown.”
BackgroundLanguage (Technology) is Power: A Critical Survey of “Bias” in NLP
Blodgett, S. L., Barocas, S., Daumé III, H., & Wallach, H. (2020) · Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics · doi:10.18653/v1/2020.acl-main.485 · Cited by 1,050 · cited 1×
“, 2020), and a lack of consensus about how to conceptualize or operationalize such harms (Blodgett et al., 2020; Jacobs and Wallach, 2021).”
BackgroundSelf-citation
Research works printed but not linked: 1
- David J. Schneider. 2005. The Psychology of Stereotyp- ing. Guilford Press.
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“They also highlight the presence of ambiguities and unstated assumptions of researchers that affect the conceptualization and operationalization of stereotypes [8].”
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“…in word embeddings [11, 15] and across many tasks, such as language modeling [16, 59, 77], machine translation [91], and question answering [84], with critiques pointing out that benchmarks aimed at measuring stereotyping may fail to meaningfully conceptualize and operationalize stereotyping [10].”
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“Similar observations have been made for operationalisation of related concepts, such as bias and stereotypes (Blodgett et al., 2021), and value alignment (Irving and Askell, 2019).”
BackgroundBias assessment for experts in discrimination, not in computer science
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“The metrics we use to measure bias are known to have limitations (Badilla et al., 2021) and the benchmarks existing in the area (Blodgett et al., 2021).”
MethodTargeting the Source: Selective Data Curation for Debiasing NLP Models
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“Excessive reliance on benchmarks can reinforce stereotypes and biases by failing to capture the diversity of the real world, limiting model effectiveness in new or different situations [21].”
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“From papers of this broader research tradition, most closely related to our paper is the work by Blodgett et al. (2021), which highlights potential issues with the oper-ationalizations of bias in bias benchmark datasets.”
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“We also acknowledge that the design process for the synthetic Stereotypes dataset could benefit from recommendations in the literature to avoid potential pitfalls associated with evaluation corpus design [10, 11].”
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“While the definition of benchmarks and bias measures itself remains challenging (Blodgett et al., 2021; Antoniak and Mimno, 2021; Delobelle et al., 2022), these analyses help to detect that models such as GPT-2 (Radford et al., 2019), RoBERTa (Liu et al., 2019) and BERT (Devlin et al., 2019) are…”
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“While Blodgett et al. [3] identify limitations in CrowS-Pairs, including dataset noise and sentences that fail to highlight relevant attribute aspects, we include it in our study due to its continued widespread use [22].”
Does It Lead to Prejudice or Discrimination? An Assessment of Stereotypes in LLM Decision-Making
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“There are also critiques of evaluation benchmarks for which such assumptions are ambiguous [23].”
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“This structured approach contrasts with common measurement practices in the machine learning community, where researchers often move from abstract concepts (like “toxicity” or “reasoning ability”) to measurement instruments without precisely defining those concepts [6, 12, 25].”
Self-citationAutomated bias blind spots: Examining stereotype associations in VLM-as-a-judge paradigms
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“The lack of consistency from intrinsic to extrinsic downstream settings may also result from benchmark validity issues [3, 8], showing that bias benchmarks are not the panacea to algorithmic discrimination, especially when model-reinforced stereotypes correspond to different kinds of experiential…”
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Reina, L. A. V., Ruiz-Fernández, V., & Falcão, J. (2026) · Compàs d amalgama · doi:10.1344/compas2026.13.8
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Metadata
- Authors
- Blodgett, S. L., Lopez, G., Olteanu, A., Sim, R., & Wallach, H.
- Affiliations
- Microsoft (United States) (US)
- 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
- 1004-1015
- OpenAlex
- W3172415559
- Cited by
- 147
- References
- 29
- Citations by year
- 2026: 13 · 2025: 17 · 2024: 21 · 2023: 55 · 2022: 34 · 2021: 7
- FWCI
- 26.22
- Type
- conference paper
- Language
- en
- Open access
- gold
- doi.org
- Licence
- cc-by
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Ethics and Social Impacts of AI
- Physical Sciences › Computer Science › Artificial Intelligence › Topic Modeling
- Physical Sciences › Computer Science › Artificial Intelligence › Hate Speech and Cyberbullying Detection
- Keywords
- stereotyping, Norwegian language, bias in language models, dataset evaluation, fairness metrics
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