Find unequal performance across groups, handle personal data carefully, and document models honestly.
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.
4 lessons · 58 min
The points in a project where bias and harm get in.
Break a model's results down by group and compare.
Collect less, protect what you keep, and remember models can leak.
Document what a model is for, how it was tested, and where it fails.
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Inventories, risk tiers, approvals and reviews that are proportionate rather than theatrical.
Personal data in training sets and in prompts: the questions to answer first, what models leak, what de-identification actually buys, and how to handle an erasure request.
Telling people a model was used, and giving a reason they can act on.