AI Safety
AI safety is the field focused on ensuring AI systems behave as intended, avoid harmful outcomes, and remain robust and controllable as capabilities scale. It spans technical research and practical governance.
In Simple Terms
Think of it as seat belts and crash tests for AI: built-in limits and checks so systems stay safe as they get more capable.
Detailed Explanation
AI safety addresses misuse (e.g., malicious use of powerful models), misalignment (systems optimizing for the wrong goal), and robustness (failure under distribution shift or adversarial inputs). Research includes value alignment, interpretability, and scalable oversight. In practice, teams apply safety through design (safety-by-design), testing (red teaming, evals), deployment controls (access, monitoring), and policy. Priorities depend on system risk level and context.
Related Terms
Artificial Intelligence
The simulation of human intelligence processes by machines, especially computer systems.
Read moreMachine Learning
A subset of AI that enables systems to learn and improve from experience without being explicitly programmed.
Read moreBias in AI
Bias in AI is systematic error or unfairness in how a model treats individuals or groups, often reflecting skewed data or flawed design. It can worsen existing inequalities if left unchecked.
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