Python & ML Libraries for Safety Research

Python Programming for AI Safety

Practical programming skills to implement and test AI safety concepts.

Core Python Skills

• Python Fundamentals: Data structures, functions, classes
• NumPy: Efficient numerical computation
• PyTorch/TensorFlow: Building and analyzing neural networks
• Visualization: Matplotlib, Seaborn for safety analysis

Safety-Specific Libraries

• Interpretability tools (Captum, LIME, SHAP)
• Adversarial robustness libraries
• Safety evaluation frameworks
• Experiment tracking and reproducibility

Best Practices

• Writing clean, documented code
• Version control with Git
• Testing and validation
• Reproducible research practices