AI safety

Rigorous technical and policy work ensuring advanced AI systems are aligned with human values and robustly protected against catastrophic, societal-scale risks.

AI safety is the interdisciplinary field focused on preventing accidents, misuse, or unintended harm from increasingly capable AI. The core technical challenge is 'alignment': ensuring AI goals match human values, thereby preventing emergent behaviors like power-seeking or deception. Key efforts also include robustness testing, bias mitigation, and developing explainable AI (XAI) frameworks (Source 1.1, 1.3). Major organizations, including the non-profit Center for AI Safety (CAIS) and government bodies like the US AI Safety Institute (AISI) within NIST, are prioritizing research into existential risks and establishing governance standards (Source 2.3, 2.7). The goal is to develop science-based safety practices that keep pace with rapid AI development, managing risks from data breaches and algorithmic bias up to potential catastrophic outcomes (Source 1.3, 2.7).

https://safe.ai