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Cited by 180Ethics and Social Impacts of AITopic ModelingHate Speech and Cyberbullying Detection

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Detecting Emergent Intersectional Biases: Contextualized Word Embeddings Contain a Distribution of Human-like Biases

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Guo, Wei, Caliskan, Aylin·2021·Proceedings of the 2021 AAAI/ACM Conference on AI, Ethics, and Society
DOI 10.1145/3461702.3462536
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Abstract

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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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)
DOI
10.1145/3461702.3462536
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
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

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