Deciphering the Algorithm: Bridging Transparency with Explainable AI in Data Engineering

Abstract

In the era of complex algorithms and data-driven decision-making, the need for transparency and interpretability in Artificial Intelligence (AI) systems has become paramount. This paper delves into the intricate realm of Explainable AI (XAI) within the context of data engineering, aiming to bridge the gap between algorithmic complexity and human comprehension. As algorithms grow in sophistication, so does the demand for clear insights into their decision-making processes. We explore the intersection of transparency and Explainable AI, presenting methodologies that unravel the intricacies of algorithms while maintaining their effectiveness in data engineering applications. Our study investigates how XAI enhances the trustworthiness of AI systems and facilitates their integration into critical decision-making processes.