Large Language Models and AI Ethics: Addressing Bias and Fairness in Intelligent Systems

Abstract

Large Language Models (LLMs) have revolutionized natural language processing, enabling sophisticated applications across various domains. However, their deployment raises critical ethical concerns, particularly around bias and fairness. LLMs are trained on vast datasets that often reflect the biases present in society, leading to the reinforcement of stereotypes and unequal treatment of different groups. This issue is compounded by the opacity of these models, making it challenging to identify and mitigate biased outputs. Addressing these concerns requires a multifaceted approach, including developing more transparent algorithms, crating diverse and representative training data, and implementing robust evaluation frameworks that prioritize fairness. Moreover, ongoing collaboration between technologists, ethicists, and policymakers is essential to ensure that the development and deployment of LLMs contribute to equitable and just outcomes in society.