Generative AI in the Workplace: Policies, Ethics, and Risks

Generative AI in the Workplace: Policies, Ethics, and Risks

This course is part of Navigating Disruption: Generative AI in the Workplace Specialization

Instructor: Josh Pasek

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3 modules

Gain insight into a topic and learn the fundamentals.

Beginner level

Recommended experience

6 hours to complete

Flexible schedule

Learn at your own pace

3 modules

Gain insight into a topic and learn the fundamentals.

Beginner level

Recommended experience

6 hours to complete

Flexible schedule

Learn at your own pace

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

Skills you'll gain

Tools you'll learn

Generative AI

Details to know

Shareable certificate

Add to your LinkedIn profile

Assessments

1 assignment

Taught in English

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This course is part of the Navigating Disruption: Generative AI in the Workplace Specialization

When you enroll in this course, you'll also be enrolled in this Specialization.

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There are 3 modules in this course

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.

Welcome to the course! In this module, we will begin with an overview of this fourth and final course in the Navigating Disruption series, Generative AI in the Workplace: Policies, Ethics, and Risks. After offering some your concerns related to AI adoption on your workplace, we’ll continue to discuss the inherent risks associated with AI adoption and how industry leading organizations are working to address such risks in their models. Let’s get started!

What's included

2 videos4 readings2 discussion prompts

In this module, we'll explore the importance of trustworthy AI and how deepfakes threaten to undermine trust in generative AI. We'll then be joined by a guest speaker who will share with us perspectives about how the professionals in law are thinking about the products of AI. We'll close this module with an examination why it is important to ensure that AI models are transparent and can be explained, both for mitigating harms that can be produced and so that users can understand how their queries are being processed.

What's included

5 videos6 readings3 discussion prompts

5 videosTotal 44 minutes

When Can You Trust an AI Output?6 minutes
Considerations When Deciding to Use AI9 minutes
AI & the Law15 minutes
How Can AI Do Harm?8 minutes
Transparency and Explainability7 minutes

6 readingsTotal 75 minutes

Dangers Associated with Deepfakes10 minutes
Deepfakes Implications for Corporations10 minutes
Comparing AI Policies Across Nations20 minutes
AI and Copyright Law10 minutes
AI and Data Risks10 minutes
Explainable AI15 minutes

3 discussion promptsTotal 45 minutes

Reflection on Deepfakes & Trustworthy AI15 minutes
Reflecting on AI Policies15 minutes
Reaction to Explainable AI15 minutes

In this final module, we'll summarize the key concepts and ideas we've discussed throughout the course. In our final graded assignment, you will revisit you first discussion post in which you shared some of the risks you are concerned about related to AI adoption in your workplace, and offer some perspectives on how your thinking has changed through our course conversations.

What's included

4 videos6 readings1 assignment1 discussion prompt

4 videosTotal 30 minutes

Relying on Randomness & a Changing Platform9 minutes
The Risks of Overreliance8 minutes
Where is AI Going?8 minutes
Back to the Old Disruptive Technologies5 minutes

6 readingsTotal 50 minutes

Understanding Out-of-Sample Errors (Part 1)10 minutes
Understanding Out-of-Sample Errors (Part 2)10 minutes
Understanding Out-of-Sample Errors (Part 3)5 minutes
Challenge of Insufficient Training Data5 minutes
End-of-Course Survey10 minutes
Continue your AI education with the AI Collection from Michigan Online10 minutes

1 assignmentTotal 20 minutes

Reflecting on Your Concerns20 minutes

1 discussion promptTotal 10 minutes

Lessons Learned from the Teachable Machine10 minutes

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Instructor

Josh Pasek

4 Courses2,248 learners

Offered by

University of Michigan

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