The gap between prototypes and responsible production deployments is growing. This gap is increasingly caused by bias, lack of transparency, privacy failures, and poor operational control over both decision-making and data use. This session will argue that "Responsible AI" is not simply a set of policies or compliance-related checklists; rather, it is an engineering discipline.
Drawing upon large-scale production environments within enterprises, this session presents examples of how governance mechanisms, risk management practices, and reliability engineering practices can be incorporated into AI architectures, and workflow processes across the entire AI system lifecycle—from data collection and system development, through deployment and ongoing operation, to maintenance and continuous improvement.

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