Section 133 · Chapter 16, Introspection: White-Box Testing Networks
Sparse Autoencoders for AI Testing
Sparse autoencoders can separate messy raw activations into more interpretable features that may become useful monitoring signals.
monitoringRAGactivationsparse autoencodersparse autoencoders
What to do
- Test their boundaries with positive, negative, ambiguous, and adversarial examples before using them as monitors or release evidence.
- Track feature stability across model versions, prompt distributions, quantization, fine-tunes, and languages.
- Define runnable checks that exercise monitoring, RAG, and activation.
Evidence to preserve
- Track feature stability across model versions, prompt distributions, quantization, fine-tunes, and languages.
- Preserve the inputs, versions, configurations, raw outcomes, and results for monitoring, RAG, activation, sparse autoencoder needed to reproduce work on Sparse Autoencoders for AI Testing.
- Report results for monitoring, RAG, activation, sparse autoencoder by relevant slice, separate blocker failures from averages, state uncertainty and blind spots, and connect the result to a release decision.
Expert note
At scale, SAE features need their own evals. Track feature stability across model versions, prompt distributions, quantization, fine-tunes, and languages. A feature that is interpretable in one setting may drift in another.
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Cite this page
Jason Arbon. "Sparse Autoencoders for AI Testing." Testing AI Knowledge Edition, section 133.
https://jarbon.ai/testing-ai/knowledge/ch133-sparse-autoencoders.html