Responsible AI in practice
Find unequal performance across groups, handle personal data carefully, and document models honestly.
What you'll learn
- Identify where harm can enter an AI system
- Measure a model's performance across groups
- Apply basic privacy practices to training data
- Write a useful model card
About this course
Responsible AI is not a separate project — it is a set of checks built into how you gather data, train, evaluate and ship models. This course makes those checks concrete, using the Adult census dataset to measure how a model treats different groups.
The code examples assume you have done Your first classifier with scikit-learn or have similar experience.
Before you start
- Your first classifier with scikit-learn (or equivalent)
Course content
4 lessons · 58 min
-
1
Where harm comes from
The points in a project where bias and harm get in.
Free preview 12 min -
2
Measuring fairness on the Adult dataset
Break a model's results down by group and compare.
20 min -
3
Privacy and personal data in training sets
Collect less, protect what you keep, and remember models can leak.
14 min -
4
Writing a model card
Document what a model is for, how it was tested, and where it fails.
12 min
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Related guides
All Responsible AI guides →-
What Is Responsible AI?
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Understanding Bias in AI Systems
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Fairness Metrics for Machine Learning
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Privacy in AI Projects
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