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.
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 1: Introduction to the Course
- Reading: Welcome to the Course & Course Syllabus
- Discussion Prompt: Introduce Yourself
- Reading: Help Us Learn About You!
- Reading: Module Introduction
- Video: Why Governance
- Video: Continuum of Governance Approaches
- Video: Emerging Governance Frameworks
- Video: Risk and Impact Assessments
- Video: Prioritization
- Discussion Prompt: Reflection: Governance Frameworks
Module 2: Strategic Alignment, Cost Analysis, and Stakeholder Mapping
- Reading: Module Introduction
- Video: Strategic Alignment & Stakeholders
- Video: Value and Cost
- Video: Interview with Susannah Shattuck: Importance of GenAI Governance
- Discussion Prompt: Reflection: Deployment Opportunities
Module 3: Applying Responsible AI Principles to Generative AI Decisions
- Reading: Module Introduction
- Video: Build - Open Source - Integrate Decisions
- Video: Model Access Considerations
- Video: Transparency Considerations
- Video: Risk Management & Use Case Restrictions
- Reading: Supplemental Resources
- Reading: Post-course Survey
- Reading: Staying Current with Responsible Generative AI and Governance
Grading Policy
Learners must complete all required assessments to pass the course. An overall score of 80% or higher is required to earn the certificate. The course grade is based on two knowledge checks worth 40% each and two honor code assignments worth 10% each for a total of 100%.
Course content developed by U-M faculty and managed by the university. Faculty titles and affiliations are updated periodically.
Intermediate Level
No prerequisites are required.
