Key terms and concepts
AI Safety - Refers to methodologies, practices, and policies ensuring AI systems perform intended functions without causing unintended harm.
Data Security - Protective measures and protocols to safeguard data from unauthorized access, theft, corruption, or loss.
Data Privacy - The right of individuals to control their personal information and its use by AI systems, ensuring compliance with legal and ethical standards.
Risk Management Strategies - Approaches to assess and mitigate potential safety risks associated with AI applications.
Safety Standards - Established guidelines and criteria to ensure AI systems' safety and reliability.
Risk Assessment - The process of identifying and analyzing potential risks and harmful outcomes that AI systems might cause.
Safety-by-Design - Incorporating safety considerations into the AI development process to prevent unsafe outcomes.
Robust Testing - Comprehensive testing of AI systems to ensure they can handle unexpected situations without causing harm.
Monitoring - Continuous observation of AI systems to detect and address safety concerns in real-time.
Human Oversight - Maintaining human control over AI systems to intervene and prevent or mitigate unsafe situations.