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

arXiv: https://arxiv.org/abs/2503.06054 · HTML: https://mlsanity.com/paper/2503.06054/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 1 of its 1 references that are in the map.
- First posted on arXiv 8 Mar 2025.

## Builds on (most cited first, 1 of 1)

- [Few-Shot Detection of Machine-Generated Text using Style Representations](https://mlsanity.com/paper/2401.06712/) (2024)

## Built on by (most cited first, 1 of 1)

- [LLM-Enhanced Rumor Detection via Virtual Node Induced Edge Prediction](https://mlsanity.com/paper/2602.13279/) (2026)

## Abstract (arXiv)

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.