Section 068 · Chapter 9, Generated Code Changes the Job
Seeing Inside Models with Interpretability Tools
Confidence Engineers do not have to treat models as sealed boxes. Interpretability tools can reveal concepts, attention paths, neuron activity, and even let teams test temporary model edits.
What to do
- Define runnable checks that exercise interpretability, confidence engineer, and attention.
- Set acceptable outcomes and blocker failures for interpretability, confidence engineer, and attention before running the evaluation.
- Run representative cases for interpretability, confidence engineer, and attention and preserve the failures that would change the decision.
Evidence to preserve
- Preserve the inputs, versions, configurations, raw outcomes, and results for interpretability, confidence engineer, attention, activation needed to reproduce work on Seeing Inside Models with Interpretability Tools.
- Report results for interpretability, confidence engineer, attention, activation by relevant slice, separate blocker failures from averages, state uncertainty and blind spots, and connect the result to a release decision.
Expert note
Model-inspection work should combine activation probes, attention traces, concept fingerprints, logit-lens checks, negative controls, behavioral counterfactuals, and carefully documented activation edits. Tools based on sparse autoencoders or other feature dictionaries may provide cleaner concept labels, but every interpretation still needs validation.
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Cite this page
Jason Arbon. "Seeing Inside Models with Interpretability Tools." Testing AI Knowledge Edition, section 68.
https://jarbon.ai/testing-ai/knowledge/ch068-seeing-inside-models-interpretability-tools.html