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As the capabilities of generative language models continue to advance, theimplications of biases ingrained within these models have garnered increasingattention from researchers, practitioners, and the broader public. This articleinvestigates the challenges and risks associated with biases in large-scalelanguage models like ChatGPT. We discuss the origins of biases, stemming from,among others, the nature of training data, model specifications, algorithmicconstraints, product design, and policy decisions. We explore the ethicalconcerns arising from the unintended consequences of biased model outputs. Wefurther analyze the potential opportunities to mitigate biases, theinevitability of some biases, and the implications of deploying these models invarious applications, such as virtual assistants, content generation, andchatbots. Finally, we review the current approaches to identify, quantify, andmitigate biases in language models, emphasizing the need for amulti-disciplinary, collaborative effort to develop more equitable,transparent, and responsible AI systems. This article aims to stimulate athoughtful dialogue within the artificial intelligence community, encouragingresearchers and developers to reflect on the role of biases in generativelanguage models and the ongoing pursuit of ethical AI.

Should ChatGPT be Biased? Challenges and Risks of Bias in Large Language Models