Evaluation Framework
A structured system for measuring AI model performance across multiple dimensions including accuracy, safety, fairness, robustness, and user satisfaction.
Why It Matters
A comprehensive evaluation framework prevents deploying models that excel on one metric but fail catastrophically on others — like a fast car with no brakes.
Example
A framework that tests an LLM across: factual accuracy (MMLU), coding ability (HumanEval), safety (red-team tests), bias (demographic parity), and user preference (human eval).
Think of it like...
Like a comprehensive health checkup that tests blood pressure, cholesterol, fitness, and mental health — one good score does not mean everything is fine.
Related Terms
Benchmark
A standardized test or dataset used to evaluate and compare the performance of AI models. Benchmarks provide consistent metrics that allow fair comparisons between different approaches.
Evaluation
The systematic process of measuring an AI model's performance, safety, and reliability using various metrics, benchmarks, and testing methodologies.
Accuracy
The percentage of correct predictions out of all predictions made by a model. While intuitive, accuracy can be misleading for imbalanced datasets.
Fairness
The principle that AI systems should treat all individuals and groups equitably and not produce discriminatory outcomes. Multiple mathematical definitions of fairness exist, and they can sometimes conflict.
Red Teaming
The practice of systematically testing AI systems by attempting to find failures, vulnerabilities, and harmful behaviors before deployment. Red teamers actively try to break the system.
Responsible AI
An approach to developing and deploying AI that prioritizes ethical considerations, fairness, transparency, accountability, and societal benefit throughout the entire AI lifecycle.