As AI systems move from experimentation to large-scale deployment, organizations face a growing gap between Responsible AI principles and real-world implementation. This session explores how to operationalize Ethical AI & Governance in practice—covering bias detection and mitigation, model transparency, privacy-preserving design, and compliance alignment with emerging regulations such as the EU AI Act.

Through real-world architectural patterns and deployment lessons from production AI systems, we will examine how to embed governance directly into the ML lifecycle—from data ingestion and training to monitoring and continuous evaluation. Attendees will learn practical strategies for building scalable AI systems that are not only performant, but also auditable, explainable, and aligned with regulatory and organizational standards.