Bias Detection and Correction in Text Using Bert Language Model

In recent years, bias in textual data has become a major challenge for fairness and inclusivity in AI systems. The proposed work introduces a span-level bias detection model that can find biased phrases and suggest neutral alternatives. Unlike sentence-level classifiers, this system analyzes text at the token level to identify biased segments like stereotypes, generalizations, and unfair assumptions. The model combines contextual embeddings with a fine-tuned transformer-based design inspired by the GUS-Net framework to capture subtle language cues. This approach improves text fairness and supports applications in chatbots, social media moderation, and automated writing assistants. Experimental evaluations show better precision and clarity in detecting biased spans, helping to build responsible and inclusive AI communication systems.