Enhancing Data Quality and Trust in AI Systems Through Robust Data Engineering

Abstract

The workability of AI systems in all kinds of industries lies in real-time and almost-real-time analytics in the sphere of healthcare, finance, and smart cities. Nevertheless, these systems have major problems with keeping the data of high quality because of the volatility of data streams in real-time, data islands, and unequal quality criteria of data. AI systems also rely much on data engineering processes in order to assure data integrity and consistency in data pumped into models. But, to maintain the best quality of data it cannot be limited to the technical systems, but also to governance and ethical issues, especially when it comes to instilling trust and transparency in AI models. The paper will discuss the major concerns of data quality, model transparency, and AI governance in terms of real-time systems and near-real-time systems. The research suggests a set of frameworks that combine AI-based data engineering processes, data lineage and automated data quality assurance methods to support data integrity. The paper also addresses why transparency and explainability of AI models are needed to create trust and promote ethical AI systems. Through a review of the most recommended practices in data quality control and AI governance models, this article will present an all-encompassing roadmap towards ensuring the data reliability and reliability of the AI models in the active life cycles. The suggested structures provide solutions that organizations can apply in order to defeat the obstacle of data quality, and also develop more open, ethical, and lawful AI structures.