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NLPGuard: A Framework for Mitigating the Use of Protected Attributes by
  NLP Classifiers

NLPGuard: A Framework for Mitigating the Use of Protected Attributes by NLP Classifiers

1 July 2024
Salvatore Greco
Ke Zhou
L. Capra
Tania Cerquitelli
Daniele Quercia
ArXivPDFHTML

Papers citing "NLPGuard: A Framework for Mitigating the Use of Protected Attributes by NLP Classifiers"

3 / 3 papers shown
Title
Mitigating Racial Biases in Toxic Language Detection with an
  Equity-Based Ensemble Framework
Mitigating Racial Biases in Toxic Language Detection with an Equity-Based Ensemble Framework
Matan Halevy
Camille Harris
A. Bruckman
Diyi Yang
A. Howard
31
35
0
27 Sep 2021
An Investigation of Why Overparameterization Exacerbates Spurious
  Correlations
An Investigation of Why Overparameterization Exacerbates Spurious Correlations
Shiori Sagawa
Aditi Raghunathan
Pang Wei Koh
Percy Liang
144
368
0
09 May 2020
A Survey on Bias and Fairness in Machine Learning
A Survey on Bias and Fairness in Machine Learning
Ninareh Mehrabi
Fred Morstatter
N. Saxena
Kristina Lerman
Aram Galstyan
SyDa
FaML
286
4,143
0
23 Aug 2019
1