Data Science Ethics Lecturer, School of Information
Generative AI: Governance, Policy, and Emerging Regulation
What You'll Learn
- Understand emerging governance expectations for businesses developing and implementing generative AI
- Identify relevant strategy considerations, costs, and key stakeholders for generative AI projects
- Apply responsible AI analysis to generative AI decisions
3 Modules
3 Hours
1 hr per module (approx.)
Rating
About Generative AI: Governance, Policy, and Emerging Regulation
In “Generative AI: Governance, Policy, and Emerging Regulation,” you’ll discuss governance considerations for generative artificial intelligence (AI) systems deployed in an organization and explore data management practices, transparency methods, risk and impact assessments, and management approaches that ensure generative AI is developed and deployed responsibly. The course also provides an overview of the current generative AI policy and regulatory landscapes within the United States, European Union, and G7 countries. By exploring governance issues and the current regulatory landscape regarding AI, you’ll gain a deeper understanding of how to integrate, manage, and monitor AI within your organization.
Skills You'll Gain
- AI-Based Legal Compliance
- AI Innovation
- Brand Management
- Decision Making
- Ethical AI
- Generative Artificial Intelligence
- Policy Analysis
- Responsible AI
- Risk Management
What You'll Earn
- Certificate of Completion
- Certificates of completion acknowledge knowledge acquired upon completion of a non-credit course or program.
Course content developed by U-M faculty and managed by the university. Faculty titles and affiliations are updated periodically.
Welcome Message
Welcome to Generative AI: Governance, Policy, and Emerging Regulation, the third course in the Responsible Generative AI series. This course examines how organizations can responsibly govern generative AI through emerging frameworks, policies, and oversight practices. You will explore governance models, strategic alignment, stakeholder considerations, and risk management to support informed, ethical decision-making when developing and deploying GenAI systems.
This abbreviated syllabus description was created with the help of AI tools and reviewed by staff. The full syllabus is available to those who enroll in the course.
Course Schedule
Module One: Understanding Governance in Generative AI
- Definitions and importance of AI governance
- Continuum of Governance Approaches
- Emerging Governance Frameworks
- Risk and Impact Assessments
Module Two: Strategic Alignment, Cost Analysis, and Stakeholder Mapping
- Strategic Alignment & Stakeholders
- Value and Cost
- Importance of GenAI Governance
- Prioritization
Module Three: Applying Responsible AI Principles to Generative AI Decisions
- Build - Open Source - Integrate Decisions
- Model Access Considerations
- Transparency Considerations
- Risk Management & Use Case Restrictions
Grading Policy
You must earn an overall grade of 80% to pass the course and earn the certificate. The course materials and assignments remain available for self-paced learning.
- Quiz: Module One — Honor Code for Governance Frameworks — 10%
- Quiz: Module Two — Honor Code for Deployment Opportunities — 10%
- Quiz: Module Two — 40%
- Quiz: Module Three — 40%
Intermediate Level
No prerequisites are required.
