Authors - Kaveti Nani Kartik, Tanuja Pattanshetti Abstract - The proliferation of Large Language Models (LLMs) has raised concerns about embedded social biases and violations of fairness. Previous work has explored bias detection in word embeddings, fairnessaware algorithmic interventions, and system-level auditing frameworks. However, these approaches are still scattered across datasets, evaluation strategies and implementation pipelines. In this paper, we present a comprehensive literature survey to summarize the previous work on bias detection and fairness auditing, and categorize the contributions based on multiple phases of the research. Moreover, coverage and consistency limitations on popular benchmark datasets are analyzed. To address these problems, we present a unified dataset integration pipeline and a modular bias auditing framework. Identified critical research gaps include lack of intersectional bias modeling, lack of standardized metrics, and limited scalability in real-time auditing systems.
Open Zoom