Fine-Grained Bias Detection in LLM: Enhancing detection mechanisms for nuanced biases

Abstract

Recent advancements in Artificial Intelligence, particularly in Large Language Models (LLMs), have transformed natural language processing by improving generative capabilities. However, detecting biases embedded within these models remains a challenge. Subtle biases can propagate misinformation, influence decision-making, and reinforce stereotypes, raising ethical concerns. This study presents a detection framework to identify nuanced biases in LLMs. The approach integrates contextual analysis, interpretability via attention mechanisms, and counterfactual data augmentation to capture hidden biases across linguistic contexts. The methodology employs contrastive prompts and synthetic datasets to analyze model behaviour across cultural, ideological, and demographic scenarios. Quantitative analysis using benchmark datasets and qualitative assessments through expert reviews validate the effectiveness of the framework. Results show improvements in detecting subtle biases compared to conventional methods, which often fail to highlight disparities in model responses to race, gender, and socio-political contexts. The framework also identifies biases arising from imbalances in training data and model architectures. Continuous user feedback ensures adaptability and refinement. This research underscores the importance of proactive bias mitigation strategies and calls for collaboration between policymakers, AI developers, and regulators. The proposed detection mechanisms enhance model transparency and support responsible LLM deployment in sensitive applications such as education, legal systems, and healthcare. Future work will focus on real-time bias monitoring and cross-linguistic generalization to improve fairness and inclusivity in AI-driven communication tools.