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Ensuring AI systems are reliable, controllable, and do not cause harm to people or society.
DESCRIPTION
AI safety addresses risks from AI systems, focusing on reliability, alignment with human values, and prevention of harmful outcomes. It spans robustness to adversarial inputs, interpretability, oversight, and risk management. Safety practices include red teaming, impact assessments, and policy controls aligned with regulation. Large models introduce new safety concerns like misuse, hallucinations, and emergent behaviors. AI safety complements AI security, which protects against malicious actors. It is a core pillar in responsible AI programs.
ALTERNATIVE DEFINITIONS
Wikipedia
Artificial intelligence safety is the field concerned with ensuring that AI systems operate as intended without causing unintended harm.
Emphasizes preventing unintended harm and reliable operation.
NIST
The AI Risk Management Framework provides guidance for managing risks in the design, development, use, and evaluation of AI products, services, and systems.
Frames safety through structured risk management practices.
UK AI Safety Institute
We work to ensure the safe development and deployment of AI.
Institutional mission emphasizing practical safety evaluation.
RELATED TERMS
AI Governance Responsible AI Red Teaming Adversarial Robustness Explainability Bias Mitigation AI Act Model Cards
COMPETING TERMS
AI Security Capabilities Research
RELATIONS
- Broader: Responsible AI
KNOWLEDGE AREAS
AI Governance AI Data Privacy & Compliance Data Security
TARGET AUDIENCE
AI Engineer MLOps Engineer Data Governance Manager Chief Data Officer Chief Technology Officer AI Product Manager
EXTERNAL SOURCES
- Wikipedia - Artificial intelligence safety
- NIST - AI risk management guidance
- OECD - AI principles and values
- EU - EU AI Act information
TOP QUESTIONS
What distinguishes AI Safety from AI Security?
AI Safety focuses on preventing harmful outcomes and ensuring alignment, while AI Security protects systems from adversarial threats and misuse.
Category: Definition
Why is AI Safety crucial for foundation models?
Their scale and generality introduce emergent behaviors, hallucinations, and misuse risks that require robust safety controls and oversight.
Category: Relevancy
How is AI Safety operationalized?
Through risk assessments, red teaming, monitoring, governance policies, and user safeguards such as model cards and safety prompts.
Category: Management
What are common AI Safety mistakes?
Treating safety as a one-off checklist, ignoring post-deployment monitoring, and failing to involve diverse stakeholders.
Category: Mistakes
What is a hot topic in AI Safety?
Scalable oversight using AI feedback, automated evaluations, and standardized audits aligned with regulation.
Category: Topic
How do you measure AI Safety effectiveness?
Use evaluation suites for harmful content, robustness tests, fairness metrics, and post-deployment incident tracking.
Category: Testing
What risks arise from neglecting AI Safety?
Harm to users, regulatory penalties, reputational damage, and system misuse or unintended behaviors.
Category: Risks
What frameworks support AI Safety?
NIST AI RMF, OECD AI Principles, and emerging standards from ISO and regulators.
Category: Best practices
Where should teams start with AI Safety?
Establish governance, define risk categories, adopt evaluation protocols, and integrate human oversight in development and deployment.
Category: Guidance