course
Artificial Intelligence Governance
GrundläggandeFärdighetsnivå
Uppdaterad 2026-05TheoryArtificial Intelligence2 timmar12 videos51 exercises3,200 XP4,036Uttalande om prestation
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Start with the Why and Who
You’ll begin by defining the scope of AI governance and aligning key stakeholders, from legal and risk to data science and business. Then, using tools like readiness assessments and maturity models, you'll learn how to set governance objectives that support both compliance needs and organizational strategy.Make Governance Work Every Day
Discover how to embed governance into your daily workflows through checklists, approval gates, and automated documentation. Learn to integrate governance into MLOps pipelines and tailor your approach using lightweight or heavyweight models depending on risk and scale.Scale Smarter Stay Accountable
Explore how to scale governance across teams and regions using federated models and governance platforms like Collibra’s. You’ll also learn to track governance KPIs, maintain traceability, and drive continuous improvement through monitoring and feedback loops.Förkunskapskrav
Det finns inga förkunskapskrav för den här kursen1
Foundations of AI Governance
Explore what AI governance is, how it differs from ethics and risk management, and why it’s essential for responsible AI. Learn the key components of governance systems, roles and responsibilities across teams, and how to embed accountability and oversight throughout the AI lifecycle.
2
Regulations and Frameworks in Practice
Dive into global AI regulations, including the EU AI Act and U.S. Executive Order, and learn how to identify and manage high-risk systems. Explore key governance actions like conformity assessments, model documentation, and impact assessments, and understand how self-regulation and traceability build compliance, trust, and long-term accountability.
3
Implementing Governance in Organizations
Learn how to design, embed, and scale AI governance in real-world settings. This chapter covers stakeholder alignment, workflow integration via MLOps, lightweight vs. heavyweight governance models, automation for scalability, and KPI-based monitoring strategies to drive continuous improvement and accountability across your AI systems.
Artificial Intelligence Governance
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