Generative AI for Fake News Detection in Data Architectures

Abstract

The proliferation of fake news threatens data integrity and public trust, challenging the foundations of modern data-driven systems. This chapter explores how Generative AI, particularly large language models (LLMs), can enhance fake news detection and support trustworthy data architectures. We review key techniques in misinformation detection and analyze the emerging role of GenAI in enabling automated content validation, explanation generation, and credibility assessment. The integration of these models into data pipelines introduces new opportunities but also raises concerns such as hallucinations, bias propagation, and ethical risks. We outline the main challenges and propose future research directions to design autonomous, explainable, and resilient systems capable of mitigating misinformation in real time while upholding transparency and data governance principles.