AI Ethics & Responsibility
Understand the social, legal, and organisational dimensions of responsible AI.
About this course
A comprehensive overview of AI ethics for practitioners, managers, and policymakers. Topics include algorithmic bias, explainability, data privacy, EU AI Act compliance, and organisational frameworks for responsible AI deployment. No technical background required.
Target audience: Product managers, policymakers, business leaders, ML practitioners, legal professionals
What you will learn
- AI ethics
- Responsible AI frameworks
- EU AI Act awareness
- Bias detection
- AI governance
Course syllabus
8 modules · video + case studies
- 1Why AI ethics matters: real-world harms and accountability
- 2Bias in data and models: sources, detection, and mitigation
- 3Fairness definitions and the trade-offs between them
- 4Explainability and interpretability: SHAP, LIME, and counterfactuals
- 5Privacy-preserving machine learning: differential privacy and federated learning
- 6The EU AI Act: risk categories, obligations, and compliance steps
- 7Organisational AI governance: roles, review boards, and policies
- 8AI and the future of work: displacement, augmentation, and retraining
Frequently asked questions
Is this relevant to EU AI Act compliance?
Yes. Module 6 covers the EU AI Act in detail including the risk tier system, conformity assessments, and documentation obligations for high-risk AI systems. GeraCompliance (geracompliance.com) offers deeper compliance sprint packages for organisations.
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