Set release boundaries for the 3x3 plus-sign classifier

Operational risks for the 3×3 plus-sign image classifier center on claiming 'computer vision' production readiness from 3×3 toys, plus the earlier failure mode (overfitting to one plus orientation, or leaking test grids into template design). Safety lives in executable gates, allowlists, redaction, and a named owner—not in a warning paragraph under an unsafe function.

meta={'image_id':'local-001','consent':True,'retention_days':0}
assert meta['consent'] and meta['retention_days']==0
print('process in memory; do not log pixels')

Expected evidence: stage evidence for security ops. A failed assertion means stop, investigate, and do not publish the 3x3 plus-sign classifier.

Name who can disable the feature, what data is retained, and how to roll back to the last known good artifact. Pin the reviewed configuration (versions, thresholds, allowlists) so “what shipped” is reconstructable for computer-sees.

Write the release blocker as a predicate, not a feeling: “Do not ship the 3x3 plus-sign classifier if claiming 'computer vision' production readiness from 3×3 toys.” Pair it with a passing control that shows the reviewed configuration still works for plus, blank, and distractor 3×3 grids. Name an owner and a rollback handle (git tag, docs_version, previous image).

Security pages must not paste the happy-path demo. If your gate code looks like the implementation page, replace it with a deny/allow check aimed at claiming 'computer vision' production readiness from 3×3 toys.

State the data retention rule in one line (what is stored, for how long, who can read it). Then state the kill switch (env flag, config pin, or feature owner). The 3x3 plus-sign classifier is not shippable without both, even when accuracy on a fixed four-image set; feature activations printed looks healthy.

At the safety and operations stage for computer-sees, the job is narrower than finishing a product demo. You are creating one progressive evidence piece about plus, blank, and distractor 3×3 grids that later pages inherit without redefining success. Keep that fixture small enough to inspect by hand, keep outputs copy-pasteable as text, and refuse to narrate this baseline as if it were a production SLA: pixel-sum threshold accuracy on the same four images.

For this page specifically, success looks like an executable deny gate for the lab-specific threat while still centering the user decision to classify tiny binary images as plus vs not-plus with inspectable features. If you cannot point to a file, command, or assertion that proves that for the 3x3 plus-sign classifier, stay on this page instead of advancing.

Glossary: computer vision · Glossary: convolutional neural network

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