APSIPA Transactions on Signal and Information Processing > Vol 13 > Issue 2

Bias and Fairness in Chatbots: An Overview

Jintang Xue, University of Southern California, USA, jintangx@usc.edu , Yun-Cheng Wang, University of Southern California, USA, Chengwei Wei, University of Southern California, USA, Xiaofeng Liu, Gordon Center for Medical Imaging, Department of Radiology, Massachusetts General Hospital and Harvard Medical School, USA, Jonghye Woo, Gordon Center for Medical Imaging, Department of Radiology, Massachusetts General Hospital and Harvard Medical School, USA, C.-C. Jay Kuo, University of Southern California, USA
 
Suggested Citation
Jintang Xue, Yun-Cheng Wang, Chengwei Wei, Xiaofeng Liu, Jonghye Woo and C.-C. Jay Kuo (2024), "Bias and Fairness in Chatbots: An Overview", APSIPA Transactions on Signal and Information Processing: Vol. 13: No. 2, e102. http://dx.doi.org/10.1561/116.00000064

Publication Date: 12 Feb 2024
© 2024 J. Xue, Y.-C. Wang, C. Wei, X. Liu, J. Woo and C.-C. J. Kuo
 
Subjects
 
Keywords
ChatbotsChatGPTBiasFairnessNatural Language Processing
 

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In this article:
Introduction 
History, Architectures, and Development Categories of Chatbots 
Bias Issues in Chatbots 
Fairness in Chatbot Applications 
Future Research Directions 
Conclusion 
References 

Abstract

Chatbots have been studied for more than half a century. With the rapid development of natural language processing (NLP) technologies in recent years, chatbots using large language models (LLMs) have received much attention nowadays. Compared with traditional ones, modern chatbots are more powerful and have been used in real-world applications. There are, however, bias and fairness concerns in modern chatbot design. Due to the huge amounts of training data, extremely large model sizes, and lack of interpretability, bias mitigation and fairness preservation of modern chatbots are challenging. Thus, a comprehensive overview on bias and fairness in chatbot systems is given in this paper. The history of chatbots and their categories are first reviewed. Then, bias sources and potential harms in applications are analyzed. Considerations in designing fair and unbiased chatbot systems are examined. Finally, future research directions are discussed.

DOI:10.1561/116.00000064

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APSIPA Transactions on Signal and Information Processing Special Issue - Pre-trained Large Language Models for Information Processing
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