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

Abstract

As the capabilities of generative language models continue to advance, the implications of biases ingrained within these models have garnered increasing attention from researchers, practitioners, and the broader public. This article investigates the challenges and risks associated with biases in large-scale language models like ChatGPT. We discuss the origins of biases, stemming from, among others, the nature of training data, model specifications, algorithmic constraints, product design, and policy decisions. We explore the ethical concerns arising from the unintended consequences of biased model outputs. We further analyze the potential opportunities to mitigate biases, the inevitability of some biases, and the implications of deploying these models in various applications, such as virtual assistants, content generation, and chatbots. Finally, we review the current approaches to identify, quantify, and mitigate biases in language models, emphasizing the need for a multi-disciplinary, collaborative effort to develop more equitable, transparent, and responsible AI systems. This article aims to stimulate a thoughtful dialogue within the artificial intelligence community, encouraging researchers and developers to reflect on the role of biases in generative language models and the ongoing pursuit of ethical AI.

Published on First Monday https://firstmonday.org/ojs/index.php/fm/article/view/13346/11365

References in corpus (4)

• Sequence to Sequence Learning with Neural Networks
• Training language models to follow instructions with human feedback
• Man is to Computer Programmer as Woman is to Homemaker? Debiasing Word Embeddings
• Beyond the Imitation Game: Quantifying and extrapolating the capabilities of language models

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• The Strong Pull of Prior Knowledge in Large Language Models and Its Impact on Emotion Recognition
• Are Large Language Models Really Bias-Free? Jailbreak Prompts for Assessing Adversarial Robustness to Bias Elicitation