Paper ID: 2503.06054 • Published Mar 8, 2025
Fine-Grained Bias Detection in LLM: Enhancing detection mechanisms for nuanced biases
Suvendu Mohanty
Amazon
TL;DR
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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.