As natural language processing methods are increasingly deployed in real-world scenarios such as healthcare, legal systems, and social science, it becomes necessary to recognize the role they potentially play in shaping social biases and stereotypes. Previous work has revealed the presence of social biases in widely used word embeddings involving gender, race, religion, and other social constructs. While some methods were proposed to debias these word-level embeddings, there is a need to perform debiasing at the sentence-level given the recent shift towards new contextualized sentence representations such as ELMo and BERT. In this paper, we investigate the presence of social biases in sentence-level representations and propose a new method, Sent-Debias, to reduce these biases. We show that Sent-Debias is effective in removing biases, and at the same time, preserves performance on sentence-level downstream tasks such as sentiment analysis, linguistic acceptability, and natural language understanding. We hope that our work will inspire future research on characterizing and removing social biases from widely adopted sentence representations for fairer NLP.
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Black is to Criminal as Caucasian is to Police: Detecting and Removing Multiclass Bias in Word Embeddings
Online texts—across genres, registers, domains, and styles—are riddled with human
stereotypes, expressed in overt or subtle ways.
Word embeddings, trained on these texts, perpetuate and amplify these stereotypes, and
propagate biases to machine learning models
that use word embeddings as features. In this
work, we propose a method to debias word
embeddings in multiclass settings such as race
and religion, extending the work of (Bolukbasi et al., 2016) from the binary setting, such
as binary gender. Next, we propose a novel
methodology for the evaluation of multiclass
debiasing. We demonstrate that our multiclass
debiasing is robust and maintains the efficacy
in standard NLP tasks.
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- Award ID(s):
- 1812327
- NSF-PAR ID:
- 10098353
- Date Published:
- Journal Name:
- 2019 Annual Conference of the North American Chapter of the Association for Computational Linguistics (NAACL)
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Accepted Manuscript1.0
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- The DOI is not currently available.
