An Explainable Retrieval Framework for Women Empowerment and Social Justice: A Comparative Review and Proposed Architecture

Women Empowerment and Social Justice area is one of those very important as well as potential application areas that artificial intelligence can be used for the social good. This paper presents an explainable retrieval framework that leverages methods for multi-modal context alignment, bias-aware retrieval, and interpretability-driven reasoning in the pursuit of fair decision support. The model combines structured socioeconomic data with unstructured legal and policy text to generate transparent, evidence-based outputs. We use a comparative analysis of retrieval models designed for fairness to demonstrate the key bottlenecks bias mitigation and explainability. Experiments on socio-economic and judicial case datasets demonstrate substantial improvements in retrieval accuracy, bias reduction, as well as alignment with human-labeled empowerment indices. The framework has developed a Women Empowerment Score Synthesizer (WESS) structure to give some measurable indications of effectiveness and inclusion. This research ties together artificial intelligence, ethics and social justice with an open and reproducible retrieval-augmented framework.