Publication Date: 2024/02/06
Abstract: The burgeoning integration of Artificial Intelligence (AI) into data engineering pipelines has spurred phenomenal advancements in automation, efficiency, and insights. However, the opaqueness of many AI models, often referred to as "black boxes," raises concerns about trust, accountability, and interpretability. Explainable AI (XAI) emerges as a critical bridge between the power of AI and the human stakeholders in data engineering workflows. This paper delves into the symbiotic relationship between XAI and data engineering, exploring how XAI tools and techniques can enhance the transparency, trustworthiness, and overall effectiveness of data-driven processes. Explainable Artificial Intelligence (XAI) has become a crucial aspect in deploying machine learning models, ensuring transparency, interpretability, and accountability. In this research article, we delve into the intersection of Explainable AI and Data Engineering, aiming to demystify the black box nature of machine learning models within the data engineering pipeline. We explore methodologies, challenges, and the impact of data preprocessing on model interpretability. The article also investigates the trade-offs between model complexity and interpretability, highlighting the significance of transparent decision-making processes in various applications.
Keywords: Explainable AI, Data Engineering, Interpretability, Machine Learning, Black Box, Transparency, XAI Techniques, Model Complexity, Case Studies.
DOI: https://doi.org/10.5281/zenodo.10623633
PDF: https://ijirst.demo4.arinfotech.co/assets/upload/files/IJISRT24JAN1534.pdf
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