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:
full (data-supply-chain) |
this distribution | |
|---|---|---|
| before — worst-group AUROC / gap | 0.788 / 0.128 | 0.787 / 0.130 |
| after — worst-group AUROC / gap | 0.787 / 0.129 | 0.786 / 0.131 |
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.
1 maintainer
Metadata
1 maintainer
Release files for data-supply-chain-workshop 0.1.1
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