The LATEST TRENDS AND CHALLENGES IN THE INTEGRATION OF ARTIFICIAL INTELLIGENCE AND BIG DATA: A COMPREHENSIVE LITERATURE REVIEW

LATEST TRENDS AND CHALLENGES IN THE INTEGRATION OF ARTIFICIAL INTELLIGENCE AND BIG DATA: A COMPREHENSIVE LITERATURE REVIEW

Abstract

The use of Artificial Intelligence (AI) and Big Data has become a major catalyst for innovation in various sectors. This literature review aims to identify the latest development trends in the integration of these two technologies, explore their implications for the industrial and social landscape, and analyze the accompanying ethical and technical challenges. The method used is a systematic narrative review, reviewing scientific articles published between 2020 and 2025. This comprehensive literature review analyzes the latest trends and challenges in the integration of Artificial Intelligence (AI) and Big Data. This research confirms that the convergence of these two technologies is a transformative force driving innovation. Key findings demonstrate a technical trend toward efficiency- and privacy-focused architectures, with Edge Computing and Federated Learning becoming crucial for real-time Big Data processing and data regulatory compliance. However, this progress is offset by serious ethical and security challenges. Key issues identified include algorithmic bias exacerbated by Big Data, black-box problems in AI models, and vulnerability to Adversarial Attacks. The review concludes that realizing the full potential of AI and Big Data depends on strong governance and clear ethics. Therefore, future research is recommended to focus on developing practical Explainable AI (XAI) methodologies and cyberdefense mechanisms to ensure responsible and secure technology implementation.

 Keywords: Artificial Intelligence, Big Data, Edge Computing, Federated Learning, Algorithmic Bias, AI Ethics.