Machine Learning Safety

Machine Learning Safety is a subfield of AI safety focused on ensuring that machine learning systems operate reliably, ethically, and without causing unintended harm. It addresses risks such as adversarial attacks, distributional shifts, reward hacking, and alignment failures in models. The goal is to develop techniques and frameworks that make ML systems robust, transparent, and aligned with human values.

Also known as: AI Safety, ML Security, Robust Machine Learning, Trustworthy AI, Safe AI

Why learn Machine Learning Safety?

Developers should learn ML Safety when building high-stakes applications like autonomous vehicles, healthcare diagnostics, or financial systems, where failures can have severe consequences. It's crucial for mitigating risks in large language models (e.g., bias, misinformation) and reinforcement learning agents (e.g., reward misspecification). Understanding safety principles helps prevent costly errors and ensures compliance with emerging regulations like the EU AI Act.

Learning Resources

• docs: Concrete Problems in AI Safety
• course: AI Safety Fundamentals Course
• video: Robustness and Adversarial Examples Tutorial
• book: The Alignment Problem by Brian Christian
• docs: Google's ML Safety Guide