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Ethics and Fairness in Conversational AI: A Framework for Addressing Bias in Large-Scale Language Models

Herbert Wanga

2026enconversational AIlarge language modelsethicsfairnessalgorithmic biasbias mitigation

Abstract

Language:

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 proposes 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.

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Cite This Work

@article{1d760a04-4e2f-4c37-b7af-84614cfa798d,
  title={Ethics and Fairness in Conversational AI: A Framework for Addressing  Bias in Large-Scale Language Models  },
  author={Herbert Wanga},
  year={2026},
  language={en}
}
TY  - JOUR
TI  - Ethics and Fairness in Conversational AI: A Framework for Addressing  Bias in Large-Scale Language Models  
AU  - Herbert Wanga
PY  - 2026
LA  - en
ER  -

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