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Detecting Emergent Intersectional Biases: Contextualized Word Embeddings Contain a Distribution of Human-like Biases
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With the starting point that implicit human biases are reflected in the statistical regularities of language, it is possible to measure biases in English static word embeddings. State-of-the-art neural language models generate dynamic word embeddings dependent on the context in which the word appears. Current methods measure pre-defined social and intersectional biases that occur in contexts defined by sentence templates. Dispensing with templates, we introduce the Contextualized Embedding Association Test (CEAT), that can summarize the magnitude of overall bias in neural language models by incorporating a random-effects model. Experiments on social and intersectional biases show that CEAT finds evidence of all tested biases and provides comprehensive information on the variance of effect magnitudes of the same bias in different contexts. All the models trained on English corpora that we study contain biased representations. GPT-2 contains the smallest magnitude of overall bias followed by GPT, BERT, and then ELMo, negatively correlating with the contextualization levels of the models.
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“Much of the existing literature on diversity in AI asks whether different demographic, cultural, or political groups are represented or treated fairly (Hardt et al., 2016;Barocas et al., 2023;Guo & Caliskan, 2021;Wang et al., 2025).”
From On Epistemic Diversity in Large Language Models (2026) · cites this paper as Guo & Caliskan, 2021
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“Meta-learning, or 'learning to learn', centers on equipping AI models with the ability to rapidly adapt to new tasks and domains using limited data samples [251], [252].”
From From Google Gemini to OpenAI Q* (Q-Star): A Survey on Reshaping the Generative Artificial Intelligence (AI) Research Landscape (2025) · cites this paper as [252]
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“The representational lens asks whether all groups are given proportional representation without misrepresentations such as negative stereotypes (Schw¨obel et al., 2023; Mehrabi et al., 2021; Abid et al., 2021; Bolukbasi et al., 2016; Guo & Caliskan, 2021).”
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Metadata
- Authors
- Guo, W., & Caliskan, A.
- Affiliations
- George Washington University (US)
- Venue
- Proceedings of the 2021 AAAI/ACM Conference on AI, Ethics, and Society
- Publisher
- ACM
- Published
- 2021-07-21
- Pages
- 122-133
- ISSN
- 3065-8365 (ISSN-L)
- OpenAlex
- W3034115845
- Cited by
- 180
- References
- 26
- Citations by year
- 2026: 16 · 2025: 27 · 2024: 34 · 2023: 44 · 2022: 45 · 2021: 11
- FWCI
- 30.34
- Type
- conference paper
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Proceedings of the AAAI/ACM Conference on AI Ethics and Society (publishedVersion, cc-by-nc-sa)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- 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
- intersectional bias, contextual word embeddings, social bias, language models, GPT-2, BERT, ELMo, random effects models, bias measurement, English language
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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