AI Ethics & Safety
Understand bias, fairness, safety, and the societal impact of AI systems. AI is powerful but comes with responsibility.
Level: Advanced · Category: Safety & Ethics · Estimated time: 4 hours
Prerequisites
- Machine Learning Basics
Lessons
- Algorithmic Bias & Fairness — Sources of bias, fairness metrics, and mitigation strategies.
- Interpretability & Explainability — SHAP, LIME, attention visualization, and model interpretability.
- Privacy in ML — Differential privacy, federated learning, and data anonymization.
- AI Safety & Alignment — Alignment problem, reward hacking, and safety research directions.
- Regulation, Governance & Societal Impact — AI regulation, EU AI Act, and the future of work with AI.
Topics covered
ethics, fairness, bias, safety, interpretability, privacy, alignment