Algorithmic Policy & Governance · Capstone brief
POL-08 · Shadow vs. production
Argue when a model should stay in shadow mode; define exit criteria with metrics the school could actually measure.
The question
Argue when a model should stay in shadow mode; define exit criteria with metrics the school could actually measure.
Policy / materials
One proposed use case. Metric definitions. Kill criteria as well as promote criteria.
Expected failure modes
Only upside metrics. No owner for the shadow review. Eternal shadow as avoidance.
Done looks like
Shadow protocol; promote/kill criteria; review cadence; owner role.
Five C's
CT: evidence before scale. CR: measurable exits. CO: peer red-teams metrics. CM: protocol page. CZ: people affected if promoted early.
Mentor role
Teacher or tech coach reviews measurability. School-supervised.
Rubric calibration
R1: one use case. R2: metrics defined. R3: shadow vs prod compared. R4: kill criteria. R5: one-pager. R6: refuses vanity metrics.
Two ways this goes wrong
(a) Promote on vibes. (b) Shadow forever with no review.
Credit lane fit
Lane A immediately. No verified credit claim.