Authors :
Mohan Raja Pulicharla
Volume/Issue :
Volume 9 - 2024, Issue 1 - January
Google Scholar :
http://tinyurl.com/yek8a8ph
Scribd :
http://tinyurl.com/y4pkmyz3
DOI :
https://doi.org/10.5281/zenodo.10623633
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.
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.
