Professor, Communication & Media and Political Science
Generative AI in the Workplace: Policies, Ethics, and Risks
What You'll Learn
- Identify ways generative AI algorithms can lead to biases in decision-making
- Anticipate implications of a lack of algorithmic transparency for decision-making
- Plan proactively to protect data security and user privacy
- Consider ethics at every stage of the generative AI decision-making process
3 Modules
6 Hours
2 hrs per module (approx.)
Rating
About Generative AI in the Workplace: Policies, Ethics, and Risks
As generative AI finds its place in contemporary organizational practices, it’s important for leaders and workers alike to understand the risk involved with using these systems. In “Generative AI in the Workplace: Policies, Ethics, and Risks,” you’ll consider the hurdles that need to be navigated for effective AI adoption. In this course, you will learn how to identify when uses of AI are appropriate, and better understand challenges in AI ethics and the law. You’ll also explore the transparency of AI algorithms and where the technology is headed in the future.
This is the fourth course in “Navigating Disruption: Generative AI in the Workplace,” a course series on ways to respond to new advances in artificial intelligence in the workplace and our lives.
Skills You'll Gain
- AI Agents
- AI Innovation
- Decision Making
- Ethical AI
- Generative AI Agents
- Generative Artificial Intelligence
- Responsible AI
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 in the Workplace: Policies, Ethics, and Risks, an online course and the fourth course in the Navigating Disruption: Generative AI in the Workplace series. This course examines the ethical, legal, and organizational risks of using generative AI at work. You will explore responsible use, transparency, governance challenges, and future directions to make informed decisions about AI adoption in professional settings.
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: Getting Started
- Video: Series Overview & Course Introduction
- Reading: Course Syllabus
- Discussion Prompt: Risks of AI Integration in Your Workplace
- Reading: Pre-Course Survey
- Video: Disruption Entails Risks
- Reading: OpenAI System Card
- Reading: GPT 4 System Card
- Discussion Prompt: OpenAI System Card Reflection
Module 2: Responsible AI Practices
- Video: When Can You Trust an AI Output?
- Reading: Dangers Associated with Deepfakes
- Reading: Deepfakes Implications for Corporations
- Discussion Prompt: Reflection on Deepfakes & Trustworthy AI
- Video: Considerations When Deciding to Use AI
- Video: AI & the Law
- Reading: Comparing AI Policies Across Nations
- Discussion Prompt: Reflecting on AI Policies
- Reading: AI and Copyright Law
- Video: How Can AI Do Harm?
- Reading: AI and Data Risks
- Video: Transparency and Explainability
- Reading: Explainable AI
- Discussion Prompt: Reaction to Explainable AI
Module 3: Future Hurdles
- Video: Relying on Randomness & a Changing Platform
- Video: The Risks of Overreliance
- Reading: Understanding Out-of-Sample Errors (Part 1)
- Reading: Understanding Out-of-Sample Errors (Part 2)
- Reading: Understanding Out-of-Sample Errors (Part 3)
- Discussion Prompt: Lessons Learned from the Teachable Machine
- Reading: Challenge of Insufficient Training Data
- Video: Where is AI Going?
- Video: Back to the Old Disruptive Technologies
- Reading: End-of-Course Survey
- Reading: Continue your AI education with the AI Collection from Michigan Online
Grading Policy
To pass the course and earn a certificate, learners must achieve a score of 80% or higher on the required graded assignment. The Reflecting on Your Concerns assignment comprises 100% of your course grade.
Course content developed by U-M faculty and managed by the university. Faculty titles and affiliations are updated periodically.
Beginner Level
We suggest taking Course 1, Course 2, and Course 3 before taking this course
