AI Safety Evaluation
Systematic testing of AI systems for potential harms including bias, toxicity, dangerous capabilities, and misuse potential, conducted before and during deployment to ensure safe operation.
Six national AI Safety Institutes have been established globally to coordinate safety evaluations. Standard evaluations include bias testing across demographics, toxicity benchmarks, capability evaluations for dual-use potential, and adversarial robustness testing. The Frontier Model Forum, founded by OpenAI, Anthropic, Google, and Microsoft, has established shared safety evaluation protocols. However, there is no universally accepted evaluation standard, and the pace of model development outstrips the development of comprehensive safety tests. The EU AI Act mandates safety testing for high-risk AI systems.
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Related Terms
Artificial General Intelligence (AGI)
A hypothetical form of AI that can understand, learn, and apply knowledge across any intellectual task at or above human level, rather than being specialized for specific tasks.
AI Alignment
The research field focused on ensuring AI systems behave in accordance with human values and intentions, particularly as systems become more capable.
AI Safety
The interdisciplinary field focused on preventing AI systems from causing harm, encompassing alignment, robustness, interpretability, and governance of AI technologies.
Deepfake
AI-generated synthetic media — images, video, or audio — that realistically depict events or statements that never occurred, created using deep learning techniques.
EU AI Act
The European Union's comprehensive AI regulation, which entered into force on August 1, 2024, classifying AI systems by risk level and imposing requirements from transparency disclosures to outright bans.
Foundation Model
A large AI model trained on broad data that can be adapted to a wide range of downstream tasks — examples include GPT-4, Claude, Gemini, and Llama.
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