Mitigating Gender Bias in Natural Language Processing: Literature Review

TL;DR

This paper reviews current research on recognizing and reducing gender bias in NLP, highlighting existing methods, their strengths and weaknesses, and proposing directions for future work in mitigating societal biases.

Contribution

It provides a comprehensive overview of gender bias types, analyzes current mitigation techniques, and discusses future research directions in NLP bias reduction.

Findings

Identifies four forms of gender representation bias.

Analyzes advantages and drawbacks of existing debiasing methods.

Highlights need for improved bias mitigation strategies.

Abstract

As Natural Language Processing (NLP) and Machine Learning (ML) tools rise in popularity, it becomes increasingly vital to recognize the role they play in shaping societal biases and stereotypes. Although NLP models have shown success in modeling various applications, they propagate and may even amplify gender bias found in text corpora. While the study of bias in artificial intelligence is not new, methods to mitigate gender bias in NLP are relatively nascent. In this paper, we review contemporary studies on recognizing and mitigating gender bias in NLP. We discuss gender bias based on four forms of representation bias and analyze methods recognizing gender bias. Furthermore, we discuss the advantages and drawbacks of existing gender debiasing methods. Finally, we discuss future studies for recognizing and mitigating gender bias in NLP.