GUS-Net: Social Bias Classification in Text with Generalizations, Unfairness, and Stereotypes

• URL: http://arxiv.org/abs/2410.08388v2
• Date: Thu, 17 Oct 2024 20:33:28 GMT
• Title: GUS-Net: Social Bias Classification in Text with Generalizations, Unfairness, and Stereotypes
• Authors: Maximus Powers, Umang Mavani, Harshitha Reddy Jonala, Ansh Tiwari, Hua Wei,
• Abstract summary: This paper introduces GUS-Net, an innovative approach to bias detection. GUS-Net focuses on three key types of biases: (G)eneralizations, (U)nfairness, and (S)tereotypes. Our methodology enhances traditional bias detection methods by incorporating the contextual encodings of pre-trained models.
• Score: 2.2162879952427343
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The detection of bias in natural language processing (NLP) is a critical challenge, particularly with the increasing use of large language models (LLMs) in various domains. This paper introduces GUS-Net, an innovative approach to bias detection that focuses on three key types of biases: (G)eneralizations, (U)nfairness, and (S)tereotypes. GUS-Net leverages generative AI and automated agents to create a comprehensive synthetic dataset, enabling robust multi-label token classification. Our methodology enhances traditional bias detection methods by incorporating the contextual encodings of pre-trained models, resulting in improved accuracy and depth in identifying biased entities. Through extensive experiments, we demonstrate that GUS-Net outperforms state-of-the-art techniques, achieving superior performance in terms of accuracy, F1-score, and Hamming Loss. The findings highlight GUS-Net's effectiveness in capturing a wide range of biases across diverse contexts, making it a valuable tool for social bias detection in text. This study contributes to the ongoing efforts in NLP to address implicit bias, providing a pathway for future research and applications in various fields. The Jupyter notebooks used to create the dataset and model are available at: https://github.com/Ethical-Spectacle/fair-ly/tree/main/resources. Warning: This paper contains examples of harmful language, and reader discretion is recommended.

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