9 AI Governance Case Studies: Wins, Failures, Lessons

Nine AI governance case studies examine how organizations in finance, healthcare, government, hiring, technology, aviation, and professional services either established controls or experienced failures, drawing on regulators, tribunals, research papers, news reports, and company accounts. Successful examples include Mastercard’s small centralized governance team, Duke Health’s mandatory registration and review process for patient-care algorithms, and Canada’s Department of Fisheries and Oceans’ maturity assessment and intake framework. Failures and shortcomings range from discriminatory use of nationality in the Dutch childcare benefits system and age-based automated hiring rejections at iTutorGroup to inadequate transparency in Apple Card credit decisions, Samsung employees sharing code with ChatGPT, Air Canada’s inaccurate chatbot advice, and AI-assisted Deloitte reports containing fabricated citations. Across the cases, recurring themes include the need for an inventory of AI systems, pre-deployment review, clear accountability, testing for disparate impacts, explanations and appeals for affected people, supplier oversight, and multidisciplinary governance teams. The source also presents Superblocks as a platform for governing internally built AI applications, while acknowledging that it does not replace model validation or governance of third-party AI systems.

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