Welcome to AI Safety & Alignment! This critical course covers the most important challenge in modern AI: ensuring that powerful AI systems behave in ways that are safe, beneficial, and aligned with human values. As AI becomes more capable, safety and alignment become paramount.
What is AI Safety?
AI Safety is the field focused on preventing AI systems from causing unintended harm. It encompasses technical research, policy considerations, and practical implementation strategies to ensure AI systems are robust, reliable, and controllable. As AI systems become more powerful, the stakes for getting safety right increase dramatically.
What is AI Alignment?
AI Alignment is the challenge of ensuring that AI systems pursue goals and values that are aligned with human intentions and welfare. The alignment problem asks: how do we build AI systems that do what we actually want them to do, even as they become more capable and autonomous?
Why This Matters Now
The rapid advancement of AI capabilities has made safety and alignment urgent priorities:
- Capability Growth - LLMs are becoming increasingly powerful and autonomous
- Real-World Impact - AI systems make decisions affecting millions of people
- Unintended Behaviors - Models can exhibit harmful or unexpected behaviors
- Specification Gaming - AI systems may optimize objectives in unintended ways
- Societal Trust - Public confidence requires demonstrable safety
- Regulatory Pressure - Governments worldwide are mandating AI safety measures
Key Safety Challenges
Modern AI faces several critical safety challenges:
- Harmful Content Generation - Preventing toxic, biased, or dangerous outputs
- Jailbreaking - Users finding ways to bypass safety guardrails
- Hallucinations - Models generating plausible but false information
- Goal Misspecification - Models optimizing the wrong objectives
- Deceptive Behavior - Models that learn to hide their true capabilities
- Distribution Shift - Performance degradation in unexpected scenarios
- Scalable Oversight - Evaluating systems more capable than their supervisors
The Alignment Problem
The core alignment challenge has several dimensions:
- Outer Alignment - Specifying the right objective function
- Inner Alignment - Ensuring the model internally optimizes what we want
- Value Loading - Encoding human values in a machine-readable form
- Corrigibility - Making systems that allow human correction
- Robustness - Maintaining alignment under distribution shifts
RLHF: Reinforcement Learning from Human Feedback
RLHF has become the standard approach for aligning language models:
- Supervised Fine-tuning - Training on high-quality human demonstrations
- Reward Modeling - Learning human preferences from comparisons
- RL Optimization - Using the reward model to improve the policy
- Iterative Refinement - Continuous improvement through feedback loops
Constitutional AI
Anthropic's Constitutional AI represents a breakthrough in alignment:
- Self-Critique - Models evaluate and improve their own outputs
- Principle-Based - Alignment guided by explicit constitutional principles
- Reduced Human Labor - Less dependence on human feedback at scale
- Transparency - Clear principles that can be audited and modified
Bias and Fairness
Ensuring AI systems are fair and unbiased is crucial:
- Training Data Bias - Historical biases reflected in training corpora
- Representation Bias - Underrepresentation of certain groups
- Measurement Bias - Biased metrics or evaluation criteria
- Fairness Metrics - Demographic parity, equalized odds, calibration
- Mitigation Strategies - Debiasing techniques and fairness constraints
Red Teaming and Adversarial Testing
Proactive safety testing is essential:
- Red Team Exercises - Simulating attacks to find vulnerabilities
- Adversarial Prompts - Testing edge cases and failure modes
- Automated Testing - Systematic safety evaluations
- Bug Bounties - Crowdsourcing security research
- Continuous Monitoring - Detecting issues in production
Safety Guardrails
Practical safety implementations include:
- Content Filtering - Detecting and blocking harmful outputs
- Input Validation - Screening user inputs for attacks
- Rate Limiting - Preventing abuse through quotas
- Human-in-the-Loop - Requiring human approval for sensitive actions
- Circuit Breakers - Automatic shutoffs when anomalies detected
- Audit Logging - Recording all interactions for review
Industry Practices
Leading AI companies implement safety measures:
- OpenAI - Safety teams, staged releases, usage policies
- Anthropic - Constitutional AI, harmlessness training
- Google DeepMind - Technical AI safety research
- Microsoft - Responsible AI principles and tools
- Meta - Red teaming, safety benchmarks
Regulatory Landscape
AI safety is becoming legally mandated:
- EU AI Act - Risk-based regulation of AI systems
- US Executive Order - Safety testing requirements
- UK AI Safety Summit - International cooperation
- Industry Standards - ISO, NIST, IEEE guidelines
What You'll Learn
This comprehensive course covers:
- Fundamentals of AI safety and the alignment problem
- RLHF implementation and reward modeling
- Constitutional AI and principle-based alignment
- Bias detection, measurement, and mitigation
- Red teaming methodologies and adversarial testing
- Building safety guardrails and monitoring systems
- Ethical AI development practices
- Regulatory compliance and responsible AI
- Production safety patterns and incident response
Career Opportunities
AI safety skills are in high demand:
- AI Safety Researcher
- AI Alignment Engineer
- ML Safety Specialist
- AI Ethics Consultant
- Trust & Safety Engineer
- Responsible AI Lead
Prerequisites
- Strong understanding of LLMs and transformers
- Familiarity with reinforcement learning basics
- Python programming and ML frameworks
- Understanding of evaluation metrics
- Critical thinking about technology's societal impact
By the end of this course, you'll understand the critical challenges in AI safety and alignment, and have practical skills to build safer, more aligned AI systems.
Let's work together to build AI that benefits humanity!