AI COLLECTIVE MIND
AI evaluation & synthetic data
Evaluation datasets can make intended behaviour and known limitations explicit. Synthetic and simulation sources may also be useful when their generation process, rights and fitness for the actual purpose are properly documented.
What could this include?
- Benchmarks, expert task examples and multilingual evaluations
- Contributor-approved annotations, preference ratings and specialist reviews
- Licensed simulations, digital twins and clearly labelled synthetic datasets
Potential uses to explore
- Explore domain or language-specific model evaluation
- Assess failure modes and acceptance criteria
- Study scenarios that are difficult to observe directly
Preparing a useful source
Separate training, validation and evaluation sets where the project requires it, and discuss overlap or contamination risks. Synthetic material needs clear labelling and limitations; it should not silently stand in for observed evidence.