Fetching the paper…
Reading the bibliography…
We examine whether some countries are more richly represented in embedding space than others.
Evaluating Natural Language Processing Systems
J.R. Galliers and K.S. Jones. 1993 · 1993
Earlier work this paper cites.
Aligning books and movies: Towards story-like visual explanations by watching movies and reading books
Yukun Zhu, Ryan Kiros, Rich Zemel, Ruslan Salakhutdinov, Raquel Urtasun, Antonio Torralba, and Sanja Fidler. 2015 · 2015
Earlier work this paper cites.
Man is to computer programmer as woman is to homemaker? debiasing word embeddings
Tolga Bolukbasi, Kai-Wei Chang, James Zou, Venkatesh Saligrama, and Adam Kalai. 2016 · 2016
Earlier work this paper cites.
Fine-grained analysis of sentence embeddings using auxiliary prediction tasks
Yossi Adi, Einat Kermany, Yonatan Belinkov, Ofer Lavi, and Yoav Goldberg. 2017 · 2017
Earlier work this paper cites.
The problem with bias: Allocative versus representational harms in machine learning
Solon Barocas, Kate Crawford, Aaron Shapiro, and Hanna Wallach. 2017 · 2017
Earlier work this paper cites.
Su Lin Blodgett and Brendan O’Connor. 2017 · 2017
Earlier work this paper cites.
Semantics derived automatically from language corpora contain human-like biases
Aylin Caliskan, Joanna J. Bryson, and Arvind Narayanan. 2017 · 2017
Earlier work this paper cites.
The trouble with bias
Kate Crawford. 2017 · 2017
Earlier work this paper cites.
The strange geometry of skip-gram with negative sampling
David Mimno and Laure Thompson. 2017 · 2017
Earlier work this paper cites.
Data statements for natural language processing: Toward mitigating system bias and enabling better science
Emily M. Bender and Batya Friedman. 2018 · 2018
Earlier work this paper cites.
NILC at CWI 2018: Exploring feature engineering and feature learning
Nathan Hartmann and Leandro Borges dos Santos. 2018 · 2018
Earlier work this paper cites.
How western journalists actually write about africa
Toussaint Nothias. 2018 · 2018
Earlier work this paper cites.
Gender bias in coreference resolution: Evaluation and debiasing methods
Jieyu Zhao, Tianlu Wang, Mark Yatskar, Vicente Ordonez, and Kai-Wei Chang. 2018 · 2018
Cited alongside, same era.
Identifying and reducing gender bias in word-level language models
Shikha Bordia and Samuel R. Bowman. 2019 · 2019
Cited alongside, same era.
BERT: Pre-training of deep bidirectional transformers for language understanding
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2019 · 2019
Cited alongside, same era.
How contextual are contextualized word representations? Comparing the geometry of BERT, ELMo, and GPT-2 embeddings
Kawin Ethayarajh. 2019 · 2019
Cited alongside, same era.
Model cards for model reporting
Margaret Mitchell, Simone Wu, Andrew Zaldivar, Parker Barnes, Lucy Vasserman, Ben Hutchinson, Elena Spitzer, Inioluwa Deborah Raji, and Timnit Gebru. 2019 · 2019
Cited alongside, same era.
Social biases in NLP models as barriers for persons with disabilities
Ben Hutchinson, Vinodkumar Prabhakaran, Emily Denton, Kellie Webster, Yu Zhong, and Stephen Denuyl. 2020 · 2020
Later among the works it cites.
Racial disparities in automated speech recognition
Allison Koenecke, Andrew Joo Hun Nam, Emily Lake, Joe Nudell, Minnie Quartey, Zion Mengesha, Connor Toups, John R. Rickford, Dan Jurafsky, and Sharad Goel. 2020 · 2020
Later among the works it cites.
“you are grounded!”: Latent name artifacts in pre-trained language models
Vered Shwartz, Rachel Rudinger, and Oyvind Tafjord. 2020 · 2020
Later among the works it cites.
Adhering, steering, and queering: Treatment of gender in natural language generation
Yolande Strengers, Lizhen Qu, Qiongkai Xu, and Jarrod Knibbe. 2020 · 2020
Later among the works it cites.
Hurtful words: Quantifying biases in clinical contextual word embeddings
Haoran Zhang, Amy X. Lu, Mohamed Abdalla, Matthew McDermott, and Marzyeh Ghassemi. 2020 · 2020
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Mohammad Taher Pilehvar and Jose Camacho-Collados. 2019 · 2019
Cited alongside, same era.
Moving down the long tail of word sense disambiguation with gloss informed bi-encoders
Terra Blevins and Luke Zettlemoyer. 2020 · 2020
Cited alongside, same era.
Language (technology) is power: A critical survey of “bias” in NLP
Su Lin Blodgett, Solon Barocas, Hal Daumé III, and Hanna Wallach. 2020 · 2020
Cited alongside, same era.
Interpreting Pretrained Contextualized Representations via Reductions to Static Embeddings
Rishi Bommasani, Kelly Davis, and Claire Cardie. 2020 · 2020
Cited alongside, same era.
Is your classifier actually biased? measuring fairness under uncertainty with bernstein bounds
Kawin Ethayarajh. 2020 · 2020
Cited alongside, same era.
Utility is in the eye of the user: A critique of nlp leaderboards
Kawin Ethayarajh and Dan Jurafsky. 2020 · 2020
Cited alongside, same era.
RealToxicityPrompts: Evaluating neural toxic degeneration in language models
Samuel Gehman, Suchin Gururangan, Maarten Sap, Yejin Choi, and Noah A. Smith. 2020 · 2020
Cited alongside, same era.
Abubakar Abid, Maheen Farooqi, and James Zou. 2021 · 2021
Later among the works it cites.
Datasheets for datasets
Timnit Gebru, Jamie Morgenstern, Briana Vecchione, Jennifer Wortman Vaughan, Hanna Wallach, Hal Daumé III, and Kate Crawford. 2021 · 2021
Later among the works it cites.
Fighting hate speech, silencing drag queens? Artificial intelligence in content moderation and risks to LGBTQ voices online
Thiago Dias Oliva, Dennys Marcelo Antonialli, and Alessandra Gomes. 2021 · 2021
Later among the works it cites.
Low frequency names exhibit bias and overfitting in contextualizing language models
Robert Wolfe and Aylin Caliskan. 2021 · 2021
Later among the works it cites.
Regional negative bias in word embeddings predicts racial animus–but only via name frequency
Austin van Loon, Salvatore Giorgi, Robb Willer, and Johannes Eichstaedt. 2022 · 2022
Closest in time.
Problems with cosine as a measure of embedding similarity for high frequency words
Kaitlyn Zhou, Kawin Ethayarajh, Dallas Card, and Dan Jurafsky. 2022 · 2022
Closest in time.