Mitigating Gender Bias in NLP Models through Targeted Concept Removal in Output Embeddings

A novel approach to mitigate gender bias in NLP models by leveraging explainable AI techniques to identify and remove gender-biased concepts from the model's output embeddings, while preserving overall model performance.