Data Provenance

Data provenance records origins, responsible parties, transformations and versions. AI pipelines may combine and redistribute many datasets, making source and processing history important for reproduction and review. The Data Provenance Initiative examines dataset licensing and attribution relationships.^[1]

Record originals, collection, filtering, deduplication and subsequent uses. Dataset documentation describes composition and recommended purposes, while provenance connects the actual artifacts and transformations. The records can help investigate evaluation leakage and interpret model results.

Provenance provides traceability; it does not automatically guarantee accuracy, lawful use or acceptable quality. Missing evidence should remain explicit rather than being converted into a permission or quality claim.

A fictional pipeline is raw-v1.csv → cleaned-v2.csv → index-v3 → answer-42. Record input version, transformation, output identifier, time and responsible party at each step. From 1,000 original rows, excluding 100 duplicates and 50 missing-value rows leaves 850 only if the sets do not overlap. If 20 of those 50 are already duplicates, unique exclusions total 130 and 870 remain. Simply adding exclusion counts can misreport the result. Counts and names are hypothetical.

W3C PROV-O ↗ describes entities, activities, responsible agents and their relationships.^[2] This pipeline is an application example, not a mandatory schema. Connect dataset composition and retrieval/generation stages to identify the actual answer evidence.

A URL locates material, but its contents can change. Identifiers and content checks help compare versions; they do not prove truth, ownership or permission. Retain original location, collection time, version, transformations and verified usage conditions separately. Unchecked permission remains unknown.

The Data Provenance Initiative study ↗ traces dataset origins, licenses and redistribution relationships.^[1] Documentation alone does not establish satisfaction of every usage condition. When investigating evaluation contamination, compare publication/access times with training and development records.

Follow relationships to determine which cleaned copies, summaries, embeddings, indexes and answer caches need updates. Record completed propagation and avoid presenting stale outputs as current. Deleting an original and handling derivatives are separate operations. Backup retention and removal follow actual operating policies.

Sources of stored user state can be traced similarly. Record evaluation-data versions in model reporting and actual transformation/retrieval paths in system documentation. Provenance helps identify affected results and reevaluation targets after an error.