Probability Theory & Statistics

Probability and Statistics for AI Safety

Probabilistic thinking is essential for reasoning about uncertainty in AI systems.

Fundamental Concepts

• Probability Distributions: Modeling uncertainty and randomness
• Bayes' Theorem: Updating beliefs with evidence
• Statistical Inference: Drawing conclusions from data
• Hypothesis Testing: Validating safety claims

AI Safety Applications

• Uncertainty quantification in model predictions
• Bayesian approaches to value learning
• Statistical guarantees for safety properties
• Risk assessment and probabilistic safety

Advanced Topics

• Information theory and entropy
• Causal inference for understanding AI behavior
• Monte Carlo methods for safety verification
• Probabilistic programming for safety analysis