NLP Based Explainable And Context-Aware RAG Framework For Women’s Empowerment And Social Justice

Abstract

AI architectures need to be able to deliver domain-specific guidance that is accurate, verifiable and empathetic to enable digital interventions on gender equity and social justice. Traditional Large Language Models (LLMs) are typically known for their limitations in high-stakes socioeconomic and legal guidance due to their flaws in reasoning opacity, geographic mismatch, and jurisdictional mismatch.The limitations of conventional Large Language Models (LLMs) often lie in their geographic and jurisdictional misalignment, reasoning opacity, and parametric hallucinations, which make them unsuitable for high-stakes socioeconomic and legal counseling. In this paper, a novel Explainable and Context-Aware Retrieval-Augmented Generation (XCA-RAG) framework is presented that directly addresses legal rights, economic entitlements, and welfare resources for the marginalized women. The framework consists of a hybrid dense-sparse hierarchical retriever, a domain adapted cross-encoder re-ranking module and an attribution-guided generative pipeline along with fine-grained post-hoc natural language inference (NLI) explainability. Systematically compared to a multi-jurisdictional benchmark comprising multiple statutory legal codes, judicial precedents, micro-finance schemes, and psychosocial support protocols, the proposed system achieves a Faithfulness score of 0.942, Contextual Precision of 0.891 and a BERTScore F1 of 0.914, surpassing baseline models like dense RAG and vanilla GPT-4 architectures. High relevance and empathetic framing of answers and elimination of arbitrary and unsubstantiated factual statements are also preserved, along with the development of a reliable computational model for responsible AI in social justice ecosystems