data-supply-chain-workshop

Workshop toolkit for "Building Fairness Into the Data Supply Chain" (AfroTech 2026). Public NHANES 1999-2004 cohort bundled; no network needed.

from data_supply_chain.bootstrap import cold_start
S = cold_start()          # 14,788 adults, canonical 70/30 split

Relationship to data-supply-chain

Same import name, data_supply_chain. Install one or the other, never both — they provide the same top-level package and pip will let the second overwrite the first's files.

This distribution omits the soft-kNN imputation module and uses column-median imputation instead. Everything else is identical.

What that changes

Four numbers in the §9 fairness bundle move in the third decimal:

Every other published figure is identical: reclassification rates, metformin masking counts (13 / 11 / 2), national projection (427,489), mortality-model AUROC (0.9013 / 0.9017), the insurance audit (0.776 / 0.911) and the ship-gate verdict. The bundle's conclusion is unchanged in both — the worst group stays Uninsured and the insurance gap does not close.

Check which you are running:

from data_supply_chain.preprocess import distribution_variant
distribution_variant()    # "workshop" or "full"

Licence

Apache-2.0 for code, CC BY 4.0 for workshop content.