International Journal of Advanced Research in Computer and Communication Engineering

Abstract: The rapid growth of large datasets in industries such as healthcare and finance has created a strong need for smarter tools that can both visualize data automatically and check whether algorithms are fair. In the past, researchers have worked on data visualization and fairness separately, but there is no single system that combines both in one platform. This paper reviews more than 25 recent research studies covering automated chart recommendations, fairness-aware machine learning, explainable AI, and bias detection. From this review, we identify several important gaps. Current systems do not combine visualization and fairness analysis in a single framework, they lack real-time bias monitoring, and they are often difficult for non-technical users to understand and use. To address these issues, we propose and develop a prototype called the AI-Powered Automated Data Visualization and Fairness Analysis Platform (AADVFAP). Our prototype shows that it is possible to build an integrated platform that handles both visualization and fairness analysis effectively. The proposed system is modular and scalable, and it is designed to support data scientists, domain experts, and policy decision-makers.

Keywords: Data Visualization, Algorithm Fairness, Bias Detection, Fair Machine Learning, Explainable AI, AI-Based Data Analysis, Real-Time Bias Checking, User-Friendly Analytics, Ethical Artificial Intelligence.

Downloads: | DOI: 10.17148/IJARCCE.2026.15228