📄️ Table of contents
Free discovery course on AI ethics in 6 lessons. Where bias comes from, why fairness has no single definition, what explainability can and cannot deliver, who is accountable, and what regulation now requires.
📄️ 1. Where bias comes from
Lesson 1 of the free AI ethics course: historical, representation, measurement, aggregation and deployment bias plus feedback loops, and why removing sensitive attributes makes things worse.
📄️ 2. Fairness definitions conflict
Lesson 2 of the free AI ethics course: the main fairness criteria, the impossibility result that shows they cannot all hold at once, and how to choose one for your context.
📄️ 3. Explainability and its limits
Lesson 3 of the free AI ethics course: what attribution methods actually deliver, why they are approximations rather than reasoning, and when an interpretable model is the right choice.
📄️ 4. Privacy and consent
Lesson 4 of the free AI ethics course: why consent breaks down at scale, how models memorise and leak, what inference reveals without collection, and what anonymisation cannot promise.
📄️ 5. Accountability and regulation
Lesson 5 of the free AI ethics course: why responsibility diffuses, what makes human oversight real rather than theatrical, the risk tiers in current regulation, and what a genuine review process looks like.
📄️ 6. Recap and FAQ
Lesson 6 of the free AI ethics course: a synthesis of the five lessons, a review checklist you can apply to any system, twelve frequently asked questions, and a precise vocabulary.
📄️ 7. Quiz and attestation
Test your understanding of AI ethics with a free 5-question quiz, corrected and explained: where bias comes from, why removing a sensitive column is not enough, explainability, privacy and accountability.