Artificial Intelligence, particularly Natural Language Processing, often inherits and amplifies societal biases, undermining fairness and trust. Existing bias detection tools frequently lack the granularity and transparency needed for effective mitigation, often operating as 'black boxes'. Bias Lens is a pioneering framework designed to address this critical challenge. It leverages a fine-tuned BERT-based model for multi-label token classification, enabling the identification of six distinct, potentially overlapping, bias types (Generalization, Unfairness, Stereotype, Assumption, Exclusion, Framing) directly at the word level. A key innovation lies in its integration of advanced Explainable AI (XAI) techniques, including a novel enhanced Integrated Gap Gradients (IG2). This provides unprecedented clarity, attributing bias to specific tokens and explaining why content is flagged. Achieving 93% accuracy and significantly faster inference (~8x) than baseline research models, Bias Lens translates complex analyses into actionable insights through an interactive visualization dashboard, accessible to diverse users. By delivering deep, interpretable bias detection and contributing open-source resources (validated dataset, model, XAI library), Bias Lens empowers stakeholders to proactively build fairer, more transparent, and ethically sound AI, fostering responsible technological advancement.