AI Safety
The interdisciplinary field studying how to develop AI systems that are safe, reliable, and beneficial.
Full Definition
AI safety encompasses both near-term safety (preventing current models from causing harm through misuse, hallucination, or bias) and long-term safety (ensuring advanced AI systems remain under human control and aligned with human values at superhuman capability levels). Near-term safety work includes content moderation, red-teaming, adversarial robustness, and differential privacy. Long-term safety research focuses on alignment, interpretability, scalable oversight, and formal guarantees. Major AI labs (Anthropic, OpenAI, DeepMind) have dedicated safety teams. The debate between 'move fast' and 'safety-first' approaches to AI development is one of the defining tensions in the field.
Examples
Anthropic's responsible scaling policy, which commits to conducting capability evaluations before deploying each new model and pausing deployment if dangerous capability thresholds are crossed.
Constitutional AI's critique-and-revision loop as a near-term safety mechanism that reduces harmful outputs without human labelling.
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