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The word embedding association test (WEAT) is an important method for measuring linguistic biases against social groups such as ethnic minorities in large text corpora.
Bad seeds: Evaluating lexical methods for bias measurement
Antoniak, M.; and Mimno, D. 2021 · 1904
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Divided by color: Racial politics and democratic ideals
Kinder, D. R.; Sanders, L. M.; and Sanders, L. M. 1996 · 1996
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Divided by color: Racial politics and democratic ideals
Kinder, D. R.; Sanders, L. M.; and Sanders, L. M. 1996 · 1996
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Measuring individual differences in implicit cognition: the implicit association test
Greenwald, A. G.; McGhee, D. E.; and Schwartz, J. L. 1998 · 1998
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Machine learning as a model for cultural learning: Teaching an algorithm what it means to be fat
Arseniev-Koehler, A.; and Foster, J. G. 2020 · 2003
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Intrinsic bias metrics do not correlate with application bias
Goldfarb-Tarrant, S.; Marchant, R.; Sanchez, R. M.; Pandya, M.; and Lopez, A. 2020 · 2012
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Trendminer: An architecture for real time analysis of social media text
Preotiuc-Pietro, D.; Samangooei, S.; Cohn, T.; Gibbins, N.; and Niranjan, M. 2012 · 2012
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Efficient estimation of word representations in vector space
Mikolov, T.; Chen, K.; Corrado, G.; and Dean, J. 2013 · 2013
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Characterizing geographic variation in well-being using tweets
Schwartz, H. A.; Eichstaedt, J. C.; Kern, M. L.; Dziurzynski, L.; Lucas, R. E.; Agrawal, M.; Park, G. J.; Lakshmikanth, S. K.; Jha, S.; Seligman, M. E. P.; and Ungar, L. H. 2013 · 2013
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Glove: Global vectors for word representation
Pennington, J.; Socher, R.; and Manning, C. D. 2014 · 2014
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Psychology data from the race implicit association test on the project implicit demo website
Xu, K.; Nosek, B.; and Greenwald, A. 2014 · 2014
Cited alongside, same era.
Human language reveals a universal positivity bias
Dodds, P. S.; Clark, E. M.; Desu, S.; Frank, M. R.; Reagan, A. J.; Williams, J. R.; Mitchell, L.; Harris, K. D.; Kloumann, I. M.; Bagrow, J. P.; et al. 2015 · 2015
Cited alongside, same era.
The political legacy of American slavery
Acharya, A.; Blackwell, M.; and Sen, M. 2016 · 2016
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A simple but tough-to-beat baseline for sentence embeddings
Arora, S.; Liang, Y.; and Ma, T. 2017 · 2017
Cited alongside, same era.
Semantics derived automatically from language corpora contain human-like biases
Caliskan, A.; Bryson, J. J.; and Narayanan, A. 2017 · 2017
Cited alongside, same era.
All-but-the-top: Simple and effective postprocessing for word representations
Identifying biases in politically biased wikis through word embeddings
Knoche, M.; Popović, R.; Lemmerich, F.; and Strohmaier, M. 2019 · 2019
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The geometry of culture: Analyzing the meanings of class through word embeddings
Kozlowski, A. C.; Taddy, M.; and Evans, J. A. 2019 · 2019
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Gender stereotypes are reflected in the distributional structure of 25 languages
Lewis, M.; and Lupyan, G. 2020 · 2020
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Explaining the ‘Trump Gap’in Social Distancing Using COVID Discourse
van Loon, A.; Stewart, S.; Waldon, B.; Lakshmikanth, S. K.; Shah, I.; Guntuku, S. C.; Sherman, G.; Zou, J.; and Eichstaedt, J. 2020 · 2020
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Measuring Judicial Sentiment: Methods and Application to US Circuit Courts
Ash, E.; Chen, D. L.; and Galletta, S. 2021 · 2021
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Mu, J.; Bhat, S.; and Viswanath, P. 2017 · 2017
Cited alongside, same era.
Bert: Pre-training of deep bidirectional transformers for language understanding
Devlin, J.; Chang, M.-W.; Lee, K.; and Toutanova, K. 2018 · 2018
Cited alongside, same era.
Word embeddings quantify 100 years of gender and ethnic stereotypes
Garg, N.; Schiebinger, L.; Jurafsky, D.; and Zou, J. 2018 · 2018
Cited alongside, same era.
Frage: Frequency-agnostic word representation
Gong, C.; He, D.; Tan, X.; Qin, T.; Wang, L.; and Liu, T.-Y. 2018 · 2018
Cited alongside, same era.
Decoding the style and bias of song lyrics
Barman, M. P.; Awekar, A.; and Kothari, S. 2019 · 2019
Cited alongside, same era.
Word embeddings reveal how fundamental sentiments structure natural language
van Loon, A.; and Freese, J. in press
Cited in the paper.
Leveraging the alignment between machine learning and intersectionality: Using word embeddings to measure intersectional experiences of the nineteenth century US South
Nelson, L. K. 2021 · 2021
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Towards a Comprehensive Understanding and Accurate Evaluation of Societal Biases in Pre-Trained Transformers
Silva, A.; Tambwekar, P.; and Gombolay, M. 2021 · 2021
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Automatically Characterizing Targeted Information Operations Through Biases Present in Discourse on Twitter
Toney, A.; Pandey, A.; Guo, W.; Broniatowski, D.; and Caliskan, A. 2021 · 2021
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Low Frequency Names Exhibit Bias and Overfitting in Contextualizing Language Models
Wolfe, R.; and Caliskan, A. 2021 · 2021
Later among the works it cites.