Explainable AI Techniques for Intelligent Data Pipeline Optimization

Abstract

Modern enterprises rely on intelligent data pipelines to collect, process, transform, and analyze data from diverse sources such as cloud platforms, IoT devices, enterprise systems, and social media. Traditional optimization techniques, including rule-based scheduling and heuristic resource allocation, improve efficiency but struggle to adapt to dynamic workloads, changing resource availability, and evolving business requirements. Artificial Intelligence (AI) addresses these limitations through predictive analytics, adaptive scheduling, anomaly detection, and autonomous resource optimization. However, the opaque nature of many AI models reduces transparency, trust, and regulatory compliance. This paper proposes an Explainable AI (XAI)-based Intelligent Data Pipeline Optimization Framework that integrates data preprocessing, predictive analytics, explainability, and adaptive optimization. The framework continuously monitors pipeline performance, generates optimization recommendations, and provides human-interpretable explanations for AI-driven decisions using feature attribution and model interpretation techniques. An automated feedback mechanism enables continuous learning and improvement. Experimental evaluation demonstrates enhanced optimization accuracy, reliability, scalability, interpretability, and administrator trust with minimal impact on performance. The proposed framework provides a transparent and trustworthy approach for next-generation intelligent data engineering systems.