Understanding the Language of AI Safety
AI safety comes with its own vocabulary. Here are the terms you'll encounter most often on this site, in plain language.
- AI Alignment
- The effort to make sure an AI system's goals and behavior actually match human intentions and values, rather than pursuing a literal or narrow interpretation of its instructions that produces harmful side effects.
- Explainable AI (XAI)
- Techniques and design principles that make an AI system's decisions understandable to humans, instead of the system operating as an unreadable "black box."
- Black Box Problem
- The difficulty of understanding why a complex AI model (especially deep neural networks) produced a particular output, even for the engineers who built it.
- Autonomous Weapon Systems (AWS)
- Weapons capable of selecting and engaging targets with little or no human control over the final decision to use force.
- Human-in-the-Loop
- A design requirement that a human must review, approve, or be able to override an AI system's decision before it takes effect, especially for high-stakes actions.
- Kill Switch
- A hardcoded, reliable mechanism to immediately halt or shut down an AI system, which cannot be overridden by the system itself.
- Model / Data Poisoning
- An attack in which corrupted or malicious data is introduced into an AI system's training process to manipulate its future behavior.
- Frontier AI / Frontier Model
- The most advanced, largest-scale AI models at any given time, whose capabilities are not yet fully understood or tested at deployment.
- Deepfake / Synthetic Media
- Realistic but fabricated audio, video, or images generated by AI, which can be used to impersonate real people or spread disinformation.
- Guardrails
- Built-in rules, filters, and constraints designed to stop an AI system from producing harmful, unsafe, or unauthorized outputs.
- Jailbreaking (AI)
- Techniques used to bypass an AI system's built-in safety restrictions, tricking it into producing content or actions it was designed to refuse.
- Agentic AI
- AI systems that can plan and carry out multi-step tasks autonomously — for example, browsing the web, executing code, or managing workflows — rather than simply responding to a single prompt.
- Existential Risk (X-risk)
- The category of risk in which an advanced AI system could cause catastrophic, irreversible harm to humanity as a whole, not just to individuals.
- Algorithmic Bias
- Systematic and unfair discrimination produced by an AI system, often reflecting biases present in its training data or design.
- Artificial General Intelligence (AGI)
- A hypothetical future AI system capable of understanding and performing any intellectual task a human can, across domains, rather than excelling narrowly at one task.
- Red Teaming
- The practice of deliberately probing an AI system for weaknesses, safety failures, or exploitable behavior before it is released, in order to fix them in advance.
Related Frameworks & Organizations
Educational reference only — GlobalAIRules is not affiliated with the organizations below.
EU Artificial Intelligence Act
The European Union's binding legal framework regulating AI systems by risk category.
OECD AI Principles
Intergovernmental principles for trustworthy, human-centered AI, adopted by dozens of countries.
NIST AI Risk Management Framework
A voluntary framework from the U.S. National Institute of Standards and Technology for managing risks in AI systems.
Asilomar AI Principles
A widely cited set of AI-safety and ethics principles developed by the Future of Life Institute.
UN High-Level Advisory Body on AI
A United Nations body established to recommend international AI governance approaches.
IEEE Global Initiative on Ethics of Autonomous Systems
An engineering-standards effort focused on embedding ethics into autonomous system design.