Ethical AI and Responsible Data Engineering: A Framework for Bias Mitigation and Privacy Preservation in Large-Scale Data Pipelines

As artificial intelligence (AI) increasingly permeates various sectors; this research presents a comprehensive framework addressing critical ethical challenges in large-scale AI data pipelines. Our approach integrates cutting-edge techniques from ethical AI, responsible data engineering, and privacy preservation to tackle bias, protect privacy, and enhance explainability. Key innovations include automated tools for bias detection and mitigation, advanced data anonymization methods, and systems for generating interpretable model explanations. Through case studies in finance, healthcare, and criminal justice, we demonstrate our framework’s effectiveness in improving fairness, privacy, and transparency metrics. This work provides a practical roadmap for implementing responsible AI practices, balancing innovation with ethical considerations and paving the way for more equitable and trustworthy AI systems across diverse industries. DOI: 10.55041/IJSREM10633