ARCHITECTING ETHICAL MACHINE LEARNING INFRASTRUCTURE: A SYSTEMATIC FRAMEWORK FOR TECHNICAL IMPLEMENTATION

This article presents a systematic framework for implementing ethical considerations within machine learning infrastructure, addressing the technical challenges of integrating fairness, explainability, and privacy protection mechanisms. The article examines the architectural components required for robust ethical ML systems, including automated bias detection pipelines, multi-level fairness monitoring systems, and parallel interpretation infrastructures for model explainability. The