Bias and Fairness in Chatbots: An Overview
- URL: http://arxiv.org/abs/2309.08836v2
- Date: Sun, 10 Dec 2023 23:38:28 GMT
- Title: Bias and Fairness in Chatbots: An Overview
- Authors: Jintang Xue, Yun-Cheng Wang, Chengwei Wei, Xiaofeng Liu, Jonghye Woo,
C.-C. Jay Kuo
- Abstract summary: Modern chatbots are more powerful and have been used in real-world applications.
Due to the huge amounts of training data, extremely large model sizes, and lack of interpretability, bias mitigation and fairness preservation are challenging.
- Score: 38.21995125571103
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- 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.
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