AI Safety
The research field focused on ensuring AI systems operate reliably, predictably, and without causing unintended harm. It spans from technical robustness to long-term existential risk concerns.
Why It Matters
As AI becomes more powerful and autonomous, safety becomes critical. A single AI failure in healthcare, finance, or critical infrastructure can have catastrophic consequences.
Example
Testing whether an AI medical diagnosis system handles edge cases correctly, or evaluating whether a language model can be manipulated into producing harmful instructions.
Think of it like...
Like aviation safety engineering — planes are incredibly useful, but rigorous safety protocols, testing, and redundancy are essential because the stakes are so high.
Related Terms
Alignment
The challenge of ensuring AI systems behave in ways that match human values, intentions, and expectations. Alignment aims to make AI helpful, honest, and harmless.
Red Teaming
The practice of systematically testing AI systems by attempting to find failures, vulnerabilities, and harmful behaviors before deployment. Red teamers actively try to break the system.
Guardrails
Safety mechanisms and constraints built into AI systems to prevent harmful, inappropriate, or off-topic outputs. Guardrails can operate at the prompt, model, or output level.
Robustness
The ability of an AI model to maintain reliable performance when faced with unexpected inputs, adversarial attacks, data distribution changes, or edge cases.
AI Ethics
The study of moral principles and values that should guide the development and deployment of AI systems. It addresses questions of fairness, accountability, transparency, privacy, and the societal impact of AI.
AI Governance
The frameworks, policies, processes, and organizational structures that guide the responsible development, deployment, and monitoring of AI systems within organizations and across society.