From Black Box to Clarity: Demystifying Explainable AI in Data Engineering Pipelines

Abstract

In the era of advanced artificial intelligence (AI), the need for transparency and interpretability in machine learning models has become paramount. This paper delves into the crucial transition from black-box models to transparent, explainable AI within data engineering pipelines. By demystifying the complexities of explainability, we aim to bridge the gap between sophisticated AI algorithms and human understanding, enhancing trust and facilitating wider adoption. Our study focuses on elucidating the principles and methodologies behind explainable AI, emphasizing its integration into data engineering pipelines. Through a comprehensive exploration of interpretability techniques, including model-agnostic methods and local feature importance, we provide insights into how data scientists and engineers can incorporate explainability seamlessly into their workflows.