Course 06
Align Your Model
This course tackles the critical challenge of making AI systems helpful, harmless, and honest. Learn to treat alignment as a technical engineering discipline, including RLHF, preference learning, bias evaluation, and safety techniques for African contexts.
Key Topics
- AI Alignment: Defining human values and intentions
- RLHF (Reinforcement Learning from Human Feedback)
- Mitigating toxicity and bias
- Safety evaluations and Red Teaming
- Cultural sensitivity in African contexts
Learning Outcomes
- Explore techniques to ensure model outputs align with human intent
- Understand technical methods for suppressing harmful outputs
- Integrate ethical considerations into the engineering lifecycle
- Implement alignment and safety techniques
- Address algorithmic bias technically
Practical Projects
- Train a Safety Guardrail model for African language chatbots
- Evaluate model bias using African stereotype datasets
- Implement DPO (Direct Preference Optimization)
Recommended Datasets
3 datasets are aligned with this course for hands-on exercises and projects.
AfriHate
Hate Speech Detection for 15 African Languages
Algerian Arabic
Amharic
Hausa
Igbo
+8 more
AfriStereo
African Stereotype Evaluation Dataset
English (African context)
French
OASST1
OpenAssistant Conversations for RLHF
35 languages (Multilingual)
About This Course
As AI models become more powerful, ensuring they align with human values becomes critical. This course covers techniques for making models helpful, harmless, and honest.
Key Topics
- Alignment Fundamentals: Why alignment matters
- Preference Learning: Learning from human preferences
- RLHF: Reinforcement Learning from Human Feedback
- Safety Evaluation: Red teaming and safety benchmarks
- Responsible AI: Ethical considerations in deployment
Prerequisites
- Completion of Courses 01-05 or equivalent knowledge
- Understanding of reinforcement learning basics
- Familiarity with evaluation metrics