The rapid advancement of large-scale language models (LLMs) has revolutionized conversational artificial intelligence (AI), enabling applications across healthcare, education, customer service, and beyond. However, these models often perpetuate and amplify societal biases present in their training data, raising significant ethical concerns. This article synthesizes current research on bias in LLMs, examining its sources, manifestations, and mitigation strategies. The article highlights the interdisciplinary challenges of ensuring fairness, including linguistic, cultural, and speciesist biases, and propose a framework for equitable AI development that integrates technical, governance, and participatory approaches. Key recommendations include diversifying training data, implementing algorithmic audits, fostering stakeholder collaboration, and adopting co-design methodologies. By integrating technical and ethical perspectives, this work aims to guide researchers, developers, and policymakers toward responsible AI deployment.