Explainable AI in Big Data Analytics

The growth of big data has enabled organizations to leverage artificial intelligence (AI) and machine learning (ML) for predictive and prescriptive analytics. However, the increasing complexity of AI models creates a “black-box” effect, making decision-making opaque and difficult to trust. Explainable AI (XAI) provides transparency, interpretability, and accountability, enabling stakeholders to understand model predictions. This paper investigates the integration of XAI into big data analytics, reviewing current methodologies, applications across healthcare, finance, marketing, and smart cities, and challenges in scalability, real-time interpretation, and ethical compliance. It also discusses future research directions for creating robust, transparent AI systems capable of handling massive datasets.