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Document and Evaluate AI Ethics

This course is part of multiple programs.

Instructor: LearningMate

Included with

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2 modules

Gain insight into a topic and learn the fundamentals.

Intermediate level

Some related experience required

4 hours to complete

Flexible schedule

Learn at your own pace

2 modules

Gain insight into a topic and learn the fundamentals.

Intermediate level

Some related experience required

4 hours to complete

Flexible schedule

Learn at your own pace

What you'll learn

Create comprehensive documentation and conduct ethical evaluations of large language model systems to ensure responsible AI deployment.

Skills you'll gain

Tools you'll learn

Model Deployment

Details to know

Shareable certificate

Add to your LinkedIn profile

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Gain a foundational understanding of a subject or tool
Develop job-relevant skills with hands-on projects
Earn a shareable career certificate

There are 2 modules in this course

Document and Evaluate AI Ethics is an intermediate course that equips engineers, auditors, and AI practitioners with the concrete skills to move from ethical principles to engineering practice. You will learn to create comprehensive model cards that document a system's intended use, dataset origins, performance metrics, and limitations, ensuring every stakeholder understands what the system does and where it might fail.

Next, you will master the process of conducting systematic ethics audits, using established frameworks to evaluate AI systems for bias, assess compliance, and propose actionable mitigation strategies. Through hands-on labs and analyses of real-world case studies—from the failure of Microsoft’s Tay to the internal audits at AstraZeneca—you will leave with the ability to produce professional audit reports and documentation that build trust and ensure your AI systems are deployed responsibly.

Module details

Have you ever released an AI feature only to realize no one could clearly explain what it does or where its data came from? That gap begins with missing documentation. This module shows how structured model cards build trust and accountability. You will capture a model’s purpose, dataset origins, fairness metrics, and limits, creating documentation that speaks to both engineers and stakeholders and keeps AI systems transparent.

What's included

2 videos3 readings1 assignment1 ungraded lab

2 videosTotal 14 minutes

Anatomy of a Model Card7 minutes
From Data to Disclosure – Writing with Precision6 minutes

3 readingsTotal 37 minutes

Why Documentation Defines Trust6 minutes
Common Pitfalls in Model Documentation6 minutes
Build Your First Model Card25 minutes

1 assignmentTotal 10 minutes

Model Documentation Review Quiz10 minutes

1 ungraded labTotal 60 minutes

Build Your First Model Card60 minutes

Have you ever seen an AI output that felt biased or off and wondered what caused it? Ethical issues often hide in oversight, not code. This module shows how to identify and address those risks before they escalate by applying structured audit frameworks to evaluate fairness and compliance in AI systems.