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Learn to audit machine learning pipelines for compliance and legal risks in this interactive workshop that examines the complex licensing, attribution, and governance challenges in modern AI systems. Navigate through a realistic NLP pipeline built for fake news detection to understand how pre-trained models, scraped datasets, cloud notebooks, and third-party APIs create new categories of risk for organizations. Trace the complete flow from data scraping to model deployment and content flagging while identifying critical compliance vulnerabilities across licensing requirements, attribution chains, and governance frameworks. Discover how open-source models and content blending can inadvertently violate intellectual property constraints and organizational policies, then map the most legally fragile components within AI pipelines. Gain practical experience exploring real-world scenarios where attribution gets lost in compressed files, data licensing becomes ambiguous, and model ownership remains unclear. Develop skills to assess which pipeline components pose the greatest legal accountability risks and learn to implement systematic approaches for AI governance. Walk away with a comprehensive, reusable checklist and audit framework specifically designed for evaluating AI systems, making this essential training for compliance officers, legal teams, and engineering or product leaders responsible for navigating the evolving landscape of AI governance and regulatory compliance.
