AI Capability Assessment Framework

An AI Capability Assessment Framework is an evaluation framework that systematically measures AI system capabilitys, performance levels, and behavioral characteristics.

• AKA: AI Evaluation Framework, Model Capability Framework, AI Assessment System, AI Performance Framework.
• Context:
  • It can typically evaluate Cognitive Capabilitys across multiple domains.
  • It can typically include Benchmark Suites, behavioral tests, and safety evaluations.
  • It can typically track Capability Progress toward AGI milestones.
  • It can typically inform Deployment Decisions and risk assessments.
  • It can often reveal Emergent Capabilitys and unexpected behaviors.
  • It can often standardize Performance Comparisons across models.
  • It can often detect Capability Jumps and phase transitions.
  • It can range from being a Narrow Capability Assessment to being a General Capability Assessment, depending on its scope coverage.
  • It can range from being a Automated Assessment Framework to being a Human-Evaluated Framework, depending on its evaluation method.
  • It can range from being a Public Assessment Framework to being a Private Assessment Framework, depending on its accessibility.
  • It can range from being a Static Assessment Framework to being an Adaptive Assessment Framework, depending on its test evolution.
  • ...
• Example:
  • Comprehensive Frameworks, such as:
    • DeepMind's AGI Levels Framework defining capability tiers.
    • Anthropic's Capability Evaluation including dangerous capabilitys.
    • OpenAI's GPT Evaluation Suite measuring diverse skills.
  • Specialized Assessments, such as:
    • MMLU Benchmark testing knowledge breadth.
    • HumanEval measuring coding capability.
    • TruthfulQA assessing factual accuracy.
  • Safety Assessments, such as:
    • Red Team Evaluations probing misuse potential.
    • Alignment Testing checking value consistency.
    • Robustness Evaluations testing adversarial resistance.
  • ...
• Counter-Example:
  • Training Metric, which optimizes rather than evaluates.
  • User Feedback, which collects opinions not capability measures.
  • Code Review, which examines implementation not performance.
  • Market Analysis, which assesses commercial value not technical capability.
• See: AI Evaluation, Capability Assessment, AI Benchmark, Performance Measurement, AGI Level, Safety Evaluation, Model Testing, Emergent Capability, AI Interpretability Method, AI Governance Framework.

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