All levels · 4 modules · 12 lessons
AI Ethics
Explore AI Ethics through 4 modules and 12 approachable lessons, with examples, practice, feedback, and a final hands-on project.
Written and edited by . Updated 2026-08-24.
What you will learn
- Identify affected people, potential harm, and sources of bias.
- Plan privacy, security, transparency, and meaningful human oversight.
- Turn ethical principles into concrete mitigations and monitoring.
Course outline
Module 01
Harm, Bias, and Fairness
Map who is affected, what harm may occur, and how bias can emerge throughout an AI system's use.
- Affected Stakeholders · 12 minutes
- Types of Harm · 16 minutes
- The Bias Lifecycle · 20 minutes
Practice output: Impact map
Module 02
Privacy, Security, and Rights
Limit data collection, check consent, respect intellectual property, and prevent misuse.
- Data Minimization · 12 minutes
- Consent and Intellectual Property · 16 minutes
- Security and Misuse · 20 minutes
Practice output: Data and rights checklist
Module 03
Transparency and Human Control
Explain when AI is used, why a result appears, how people can appeal, and who makes the final decision.
- Disclosure · 12 minutes
- Explanation and Recourse · 16 minutes
- Human Oversight · 20 minutes
Practice output: Human oversight plan
Module 04
Responsible AI in Practice
Set the risk level, mitigations, owner, monitoring, and response when an incident occurs.
- Risk Tiers · 12 minutes
- Mitigation and Ownership · 16 minutes
- Monitoring and Incidents · 20 minutes
Practice output: Responsible AI risk assessment
