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AI Ethics & Compliance Guide
Learn AI ethics and compliance: bias detection, fairness metrics, the EU AI Act, and GDPR applied to AI systems. Close this AI Engineering ecosystem with the responsibility framework every AI Engineer needs. Master bias detection and fairness metrics, navigate the EU AI Act (risk categories, high-risk requirements, compliance timelines), apply GDPR to AI systems (right to explanation, consent, data minimization), and build a reusable ethics checklist. This guide transforms your technical skills into responsible AI practice with actionable deliverables.
- 64
- lessons
- 8
- modules
- English · Spanish
- available in
- Yes
- certificate
- Free
- access
Outcomes
What you'll be able to do
- Analyze real AI ethics failures (hiring algorithms, facial recognition, credit scoring) and extract actionable lessons
- Implement fairness metrics (demographic parity, equalized odds) and bias testing with concrete techniques
- Apply privacy fundamentals to AI: data minimization, anonymization, consent for AI processing
- Classify AI systems under EU AI Act risk categories (unacceptable, high, limited, minimal)
- Navigate EU AI Act requirements for high-risk AI and compliance timelines 2025-2027
- Implement GDPR for AI: right to explanation, consent, data minimization, automated decision-making documentation
- Map your systems to industry standards (NIST AI RMF, IEEE 7000, ISO 42001)
- Build a reusable Responsible AI ethics checklist and review processes
- Conduct a complete ethics audit of an AI system using your framework
Before you start
What you need to bring
It's for you if...
- AI Engineers who have completed the path and need the responsibility and compliance framework
- Tech leads and architects requiring governance documentation and compliance for stakeholders
- Startups and companies deploying AI in or targeting the EU (EU AI Act applies)
- Developers who process personal data with AI (GDPR applies)
- Professionals who want to differentiate with "responsible AI" in proposals and client work
Requirements and materials
- Completed AI Engineering Path (or equivalent: RAG, agents, deployment, production practices)
- Real experience building and deploying at least one AI system
- Familiarity with ML/LLM concepts (training, inference, evaluation)
- Understanding of software lifecycle and architecture decisions
Content
The syllabus, module by module
Open any of them to see its lessons.
- 1. Module introduction: Why Ethics Matters in AI Engineering
- 2. Case Study #1: Amazon Hiring Algorithm
- 3. Case Study #2: Facial Recognition Bias
- 4. Case Study #3: Apple Card and Credit Scoring
- 5. The 4 dimensions of the cost of ignoring ethics in AI
- 6. The engineer's ethical responsibility
- 7. Ethics Impact Analysis: the framework
- 8. Mini-project: Ethics Impact Analysis
- 1. Module introduction: Bias and Fairness
- 2. Fairness Metric: Demographic Parity
- 3. Fairness Metrics: Equalized Odds and Calibration
- 4. Bias Detection: Slicing, A/B Testing and Counterfactual Testing
- 5. Impossibility Theorems and Trade-offs
- 6. Mitigation: Pre-processing Techniques
- 7. Mitigation: In-processing and Post-processing
- 8. Mini-project: Bias Audit Toolkit
- 1. Module introduction: EU AI Act Deep Dive
- 2. Unacceptable Risk: Prohibited Systems
- 3. High-Risk AI: Categories and Criteria
- 4. High-Risk Obligations: The 6 Requirements
- 5. Limited Risk: Transparency Obligations
- 6. Minimal Risk and General Purpose AI
- 7. Timelines and Penalties
- 8. Mini-project: Risk Classification
- Introduction to the Responsible AI Framework
- Designing the checklist: structure
- Checklist content: bias + fairness
- The Privacy, GDPR, and EU AI Act sections of the checklist
- Review processes
- Documentation templates
- Lightweight governance: realistic for small teams
- Project: The complete Responsible AI Framework
Where it fits
This guide is part of something bigger
It's studied inside these programs, with support and dates.
Common questions
What people usually ask
No limit. It's a free guide: come in whenever you like, as often as you like.
No. Modules run from easier to harder, but you can jump to the one you need. Progress is saved per lesson.
Whatever is needed is listed under “What you need to bring”, above. If nothing is listed there, you can start from zero.
In the Club's WhatsApp group, and every two weeks there's a live with an instructor where questions get worked through.
Yes. It's issued automatically once you finish every lesson, with a verifiable code you can share on LinkedIn.
No. This guide is self-paced with no dates. The bootcamp is live, by cohort, with work someone reviews.
Start whenever you like
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