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This training introduces AI Governance frameworks, outlining their strengths, weaknesses, and how they apply to different business scenarios. With only 39% of CEOs rating their organization's AI Governance as effective (according to IBM), understanding these frameworks can help data practitioners improve AI project practices, manage risk, and create business value. The video covers: Overview of leading AI Governance frameworks Considerations for building a business case Practical guidance for designing a custom framework Watch to learn how AI Governance frameworks can support more responsible AI use. Resources: Business Value of Leading AI Governance Frameworks blog post series: https://www.datastrategypros.com/reso... Seven leading AI Governance frameworks: Singapore's Model AI Governance Framework: https://www.pdpc.gov.sg/-/media/Files... Microsoft Responsible AI Standard: https://cdn-dynmedia-1.microsoft.com/... Council of Europe's HUDERAF for AI Systems: https://arxiv.org/pdf/2202.02776 OECD's Framework for the Classification of AI Systems: https://www.oecd.org/en/publications/... NIST AI Risk Management Framework: https://nvlpubs.nist.gov/nistpubs/ai/... Google Secure AI Framework Approach: https://services.google.com/fh/files/... AWS Cloud Adoption Framework for AI, ML, and Gen AI: https://docs.aws.amazon.com/pdfs/whit...