Definition

AI safety is about designing and testing AI so it doesn’t do anything dangerous or unpredictable. It makes sure the AI follows the rules, helps people, and doesn’t act in harmful or uncontrolled ways.

Example

“AI safety includes stopping a robot from hurting people or making sure a chatbot doesn’t give harmful advice.”

How It’s Used in AI

Used in model training, testing, and deployment. AI safety helps prevent accidents, abuse, and harmful outcomes in areas like self-driving cars, large language models, and autonomous agents. It's a key focus for researchers working on powerful AI systems like AGI.

Brief History

AI safety research began growing in the 2010s alongside deep learning. Organizations like OpenAI, Anthropic, and Alignment Research Center were created to explore how to build safe, aligned AI—especially as models became more capable.

Key Tools or Models

Tools include red teaming, adversarial testing, RLHF, guardrails, and constitutional AI. Models like Claude, GPT-4, and Gemini are trained with safety techniques to reduce risk.

Pro Tip

Safety isn’t just for future AGI—it matters right now. Even today’s tools can cause harm if misused or left unchecked.