The research and safeguards that aim to keep AI from causing harm.
AI safety covers the research and safeguards meant to stop AI from being misused or behaving in harmful, unexpected ways. It includes teaching models to refuse dangerous requests, attacking them on purpose before release to find weak spots, and keeping an eye on how they're used.
Experts disagree about which risks deserve the most attention. Some focus on problems happening now, like false information and unfair bias; others worry more about whether people can stay in control of much more powerful future systems. Countries have also started meeting about it, for example at the 2023 AI Safety Summit in the UK.
Think of it like this
Think of a roller coaster. You trust it because of many layers: lap bars, height limits and test runs every morning. AI safety works the same way: not one magic fix, but many layers stacked together.
For example
Before release, experts often try to trick a model into harmful answers on purpose. This is called red-teaming.
Records about this
Source-checked moments from AI history.
Brings the format of an IPCC-style international consensus document to the AI risk debate. A shared factual basis for regulatory debates in each country
An international AI governance milestone co-hosted by Korea
The starting point of the line of international AI safety summits, and the source of the mandate for the International AI Safety Report