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As generative AI systems, particularly large language models (LLMs), gain widespread adoption through various applications, worries around biased and discriminatory outputs have become increasingly urgent. These biases, often inherited from training statistics, can prime to biased or harmful results in automated content generation. This study presents a comprehensive method for discovering and modifying unfairness in the text outputs of generative AI models. We propose a multi-phase approach that incorporates bias detection using both rule-based and statistical metrics, followed by the application of generative model-based mitigation techniques. Our implementation leverages fine-tuning and prompt engineering strategies to reduce biased expressions while maintaining coherence and relevance in the output. Complete quantifiable calculation and qualitative study through several demographic and morphological measurements, we show the efficiency of our methodology now reducing measurable bias without significantly debasing language superiority. This work contributes practical tools and theoretical insights toward building fairer and more responsible generative AI systems.

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Bias Mitigation in Generative AI Text Outputs with Generative Models: Bias Detection Methodology and Implementation

  • 会议论文

  • 发表日期：

    2026年

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  • K. Sunitha,
  • Swati Sharma

摘要

As generative AI systems, particularly large language models (LLMs), gain widespread adoption through various applications, worries around biased and discriminatory outputs have become increasingly urgent. These biases, often inherited from training statistics, can prime to biased or harmful results in automated content generation. This study presents a comprehensive method for discovering and modifying unfairness in the text outputs of generative AI models. We propose a multi-phase approach that incorporates bias detection using both rule-based and statistical metrics, followed by the application of generative model-based mitigation techniques. Our implementation leverages fine-tuning and prompt engineering strategies to reduce biased expressions while maintaining coherence and relevance in the output. Complete quantifiable calculation and qualitative study through several demographic and morphological measurements, we show the efficiency of our methodology now reducing measurable bias without significantly debasing language superiority. This work contributes practical tools and theoretical insights toward building fairer and more responsible generative AI systems.