Audit Data Integrity
Inspect existing code, contracts, manifests, and logs; do not recompute missing
results. Report each applicable check as pass, flaw, or unverified, with a
specific evidence path or exact missing evidence.
Checks
- Establish the semantic meaning and order of every source axis. Trace each
transpose,reshape,ravel,flatten,stack, concatenation, cache, and reload that can change sample identity. Shape equality alone is insufficient. - Verify that every feature row remains aligned with its label, subject, condition, session, bin, and other grouping identifiers. Look for explicit value-level assertions on representative samples.
- Verify train/test independence at the real sampling unit. Check subject, session, site, family, temporal, repeated-measure, and augmentation leakage as applicable.
- Verify that imputation, scaling, feature selection, dimensionality reduction, resampling, threshold selection, and model selection are fitted only within training folds.
- Establish the value domain and transform contract. Confirm that training, exported artifacts, manifests, and inference use the same clipping, missing-value handling, transform, and feature order.
For a bounded parallel review, give a method-reviewer the data contract,
pipeline files, and validation evidence. Ask for evidence and candidate findings,
not a verdict.
