Interpretable AI in Data Engineering: Demystifying the Black Box Within the Pipeline.

EasyChair Preprint 14760

Abstract

As artificial intelligence (AI) continues to revolutionize data engineering, the rise of complex models has introduced the challenge of the "black box" phenomenon—where decision-making processes become opaque and difficult to interpret. This article explores the importance of interpretability in AI models within the context of data engineering, emphasizing the need to demystify these black boxes to ensure transparency, trust, and accountability. We delve into various interpretability techniques, such as feature importance, model simplification, and explanation methods like LIME and SHAP, highlighting their application in real-world data pipelines. By integrating interpretable AI at different stages of the data engineering process, professionals can enhance model debugging, optimize performance, and foster better collaboration with stakeholders. Despite the challenges and trade-offs between accuracy and interpretability, this article argues that a balanced approach is crucial for the future of data engineering, ensuring that AI-driven insights are not only powerful but also comprehensible.

Keyphrases: AI Transparency, AI in Data Pipelines., Data Engineering, Explainable AI, Interpretable AI, Model Interpretability, black-box models

@booklet{EasyChair:14760,
  author    = {Toluwani Bolu},
  title     = {Interpretable AI in Data Engineering: Demystifying the Black Box Within the Pipeline.},
  howpublished = {EasyChair Preprint 14760},
  year      = {EasyChair, 2024}}