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Quantifying and Reducing Stereotypes in Word Embeddings

Quantifying and Reducing Stereotypes in Word Embeddings

20 June 2016
Tolga Bolukbasi
Kai-Wei Chang
James Zou
Venkatesh Saligrama
Adam Kalai
ArXiv (abs)PDFHTML

Papers citing "Quantifying and Reducing Stereotypes in Word Embeddings"

18 / 18 papers shown
Do Large Language Models Understand Morality Across Cultures?
Do Large Language Models Understand Morality Across Cultures?
Hadi Mohammadi
Yasmeen F.S.S. Meijer
Efthymia Papadopoulou
Ayoub Bagheri
246
2
0
28 Jul 2025
Dissecting Bias in LLMs: A Mechanistic Interpretability Perspective
Dissecting Bias in LLMs: A Mechanistic Interpretability Perspective
Bhavik Chandna
Zubair Bashir
Procheta Sen
346
11
0
05 Jun 2025
Identifying Gender Stereotypes and Biases in Automated Translation from English to Italian using Similarity Networks
Identifying Gender Stereotypes and Biases in Automated Translation from English to Italian using Similarity Networks
Fatemeh Mohammadi
Marta Annamaria Tamborini
Paolo Ceravolo
Costanza Nardocci
S. Maghool
257
1
0
17 Feb 2025
LLMs as mirrors of societal moral standards: reflection of cultural
  divergence and agreement across ethical topics
LLMs as mirrors of societal moral standards: reflection of cultural divergence and agreement across ethical topics
Mijntje Meijer
Hadi Mohammadi
Ayoub Bagheri
227
5
0
01 Dec 2024
Revisiting The Classics: A Study on Identifying and Rectifying Gender
  Stereotypes in Rhymes and Poems
Revisiting The Classics: A Study on Identifying and Rectifying Gender Stereotypes in Rhymes and PoemsInternational Conference on Language Resources and Evaluation (LREC), 2024
Aditya Narayan Sankaran
Vigneshwaran Shankaran
Sampath Lonka
Rajesh Sharma
328
1
0
18 Mar 2024
Fairness Evaluation for Uplift Modeling in the Absence of Ground Truth
Fairness Evaluation for Uplift Modeling in the Absence of Ground Truth
Serdar Kadioğlu
Filip Michalsky
158
2
0
12 Feb 2024
Are fairness metric scores enough to assess discrimination biases in
  machine learning?
Are fairness metric scores enough to assess discrimination biases in machine learning?
Fanny Jourdan
Laurent Risser
Jean-Michel Loubes
Nicholas M. Asher
FaML
314
6
0
08 Jun 2023
Dialectograms: Machine Learning Differences between Discursive
  Communities
Dialectograms: Machine Learning Differences between Discursive Communities
Thyge R Enggaard
August Lohse
M. Pedersen
Sune Lehmann
218
2
0
11 Feb 2023
Theories of "Gender" in NLP Bias Research
Theories of "Gender" in NLP Bias ResearchConference on Fairness, Accountability and Transparency (FAccT), 2022
Hannah Devinney
Jenny Björklund
H. Björklund
AI4CE
369
93
0
05 May 2022
Towards an Enhanced Understanding of Bias in Pre-trained Neural Language
  Models: A Survey with Special Emphasis on Affective Bias
Towards an Enhanced Understanding of Bias in Pre-trained Neural Language Models: A Survey with Special Emphasis on Affective Bias
Anoop Kadan
Manjary P.Gangan
Deepak P
L. LajishV.
AI4CE
372
17
0
21 Apr 2022
Counterfactual Multi-Token Fairness in Text Classification
Counterfactual Multi-Token Fairness in Text Classification
P. Lohia
256
3
0
08 Feb 2022
Towards Neural Programming Interfaces
Towards Neural Programming InterfacesNeural Information Processing Systems (NeurIPS), 2020
Zachary Brown
Nathaniel R. Robinson
David Wingate
Nancy Fulda
AI4CE
304
5
0
10 Dec 2020
Cultural Cartography with Word Embeddings
Cultural Cartography with Word Embeddings
Dustin S. Stoltz
Marshall A. Taylor
508
47
0
09 Jul 2020
Language (Technology) is Power: A Critical Survey of "Bias" in NLP
Language (Technology) is Power: A Critical Survey of "Bias" in NLPAnnual Meeting of the Association for Computational Linguistics (ACL), 2020
Su Lin Blodgett
Solon Barocas
Hal Daumé
Hanna M. Wallach
1.0K
1,596
0
28 May 2020
Machine learning as a model for cultural learning: Teaching an algorithm
  what it means to be fat
Machine learning as a model for cultural learning: Teaching an algorithm what it means to be fatSociological Methods & Research (SMR), 2020
Alina Arseniev-Koehler
J. Foster
339
61
0
24 Mar 2020
Assessing Social and Intersectional Biases in Contextualized Word
  Representations
Assessing Social and Intersectional Biases in Contextualized Word RepresentationsNeural Information Processing Systems (NeurIPS), 2019
Y. Tan
Elisa Celis
FaML
463
258
0
04 Nov 2019
Using Word Embeddings to Examine Gender Bias in Dutch Newspapers,
  1950-1990
Using Word Embeddings to Examine Gender Bias in Dutch Newspapers, 1950-1990
M. Wevers
261
37
0
21 Jul 2019
TFW, DamnGina, Juvie, and Hotsie-Totsie: On the Linguistic and Social
  Aspects of Internet Slang
TFW, DamnGina, Juvie, and Hotsie-Totsie: On the Linguistic and Social Aspects of Internet Slang
Vivek Kulkarni
William Yang Wang
177
3
0
22 Dec 2017
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