Ethics & Safety
Ensure responsible AI. Learn about bias, fairness, interpretability, and AI alignment.
Prerequisites
Complete Level 8: MLOps
🎯What You'll Learn
- ✓Identifying and mitigating bias in AI systems
- ✓Model interpretability and explainability
- ✓Privacy-preserving machine learning
- ✓AI alignment and safety considerations
- ✓Ethical frameworks for AI development
💪Skills You'll Gain
🏆Learning Outcomes
📖Interactive Modules (10)
Bias in AI Systems
Understand bias in AI systems, fairness metrics, and techniques to mitigate algorithmic bias.
Adversarial Attacks
Learn adversarial attacks that fool AI models and defense strategies against them.
Explainable AI (XAI)
Master explainable AI (XAI) techniques: LIME, SHAP, and interpreting model decisions.
Privacy-Preserving ML
Understand privacy-preserving machine learning, differential privacy, and federated learning.
Model Watermarking
Learn model watermarking techniques to protect intellectual property and detect model theft.
Jailbreaking LLMs
Explore prompt injection, jailbreaking techniques, and LLM security vulnerabilities.
AI Alignment Challenges
Understand AI alignment challenges: ensuring AI systems pursue intended human values.
AI Governance Frameworks
Learn AI governance frameworks, regulations, and organizational AI ethics policies.
Environmental Impact
Understand environmental impact of training large models and sustainable AI practices.
Responsible AI Checklist
Master responsible AI principles, checklist for ethical AI development and deployment.