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AI ethics is a practical engineering concern. This course covers bias detection, fairness metrics, interpretability techniques, safety frameworks, and the evolving regulatory landscape. You’ll conduct a real bias audit as the final project.

✅ What’s Inside:

  1. What AI Alignment Actually Means
  2. Types of Model Bias
  3. Fairness Metrics and Trade-offs
  4. Interpretability Methods (SHAP, LIME)
  5. Red-Teaming AI Systems
  6. Constitutional AI Overview
  7. Regulatory Landscape 2026
  8. Data Privacy in AI
  9. Responsible Disclosure
  10. AI Safety Principles
  11. Organizational Ethics Practices
  12. Project: Bias Audit an AI System