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EthicsBeginner2h
AI Ethics & Responsible Deployment
Build AI you'd be proud to put your name on
About this course
Bias, fairness, privacy, accountability — the human side of AI. Frameworks and case studies that help you make better decisions.
What you'll learn
- Identify common sources of bias
- Apply a fairness-aware design checklist
- Understand privacy and data minimisation
- Communicate risks to stakeholders
Curriculum
1
Where bias comes from
10 min
2
Fairness in practice
12 min
3
Privacy by design
10 min
4
Accountability and transparency
10 min
Instructor · Dr. Lena Hoffmann
Course Assignment
An Honest Ethics Audit
Take any AI system in the wild and audit it like you mean it.
Brief
Pick one shipped AI product. Produce a short ethics audit covering: bias surface area, fairness definition you'd hold it to, privacy posture, and accountability gaps. End with 3 specific recommendations the team could ship next quarter.
Deliverable
A 1-2 page audit document with named recommendations and at least one source per claim.
Rubric
- Bias sources are traced to specific stages (data / labels / metrics / deployment)
- A specific fairness definition is named and defended
- Privacy posture is evaluated against data-minimisation principles
- Accountability gaps are concrete (e.g., 'no appeal path')
- Recommendations are shippable in a single quarter
Complete every lesson lab first — they're the raw material for this assignment.