Dataset Provenance and Documentation Assurance Framework for High-Risk AI
A completed Data and AI engineering study for tracing dataset origin, rights, transformations, versions, quality, limitations and changes in high-risk AI projects.
Project definition
Problem statement
High-risk AI can depend on datasets with unknown sources, unclear permissions, undocumented transformations, weak labels, population gaps, leakage or silent version changes.
The engineering problem is to connect every important dataset claim to an identifiable, versioned and reviewable piece of evidence without treating documentation as proof.
Project objectives
- Define a complete dataset lifecycle from intended purpose to withdrawal.
- Compare eight documentation, provenance, versioning, quality, governance and regulatory frameworks.
- Evaluate source, rights, lineage, quality, representation, annotation and split integrity separately.
- Keep hard evidence gates visible beside weighted scores.
- Quantify uncertainty and rank missing evidence through workflow FMEA.
- Produce a practical evidence and review structure for a real dataset extension.
Project structure
Project components
Framework register
Declares narrative, W3C PROV, DCAT, artifact-versioning, governance, ISO 5259, regulatory and lifecycle-wide approaches.
Risk gates
Prevents weak origin, rights, lineage, quality, bias or change evidence from being hidden by a strong average.
Case model
Evaluates 768 framework, risk, context and evidence combinations.
Decision analysis
Compares balanced, rights, quality and traceability priorities with sensitivity and uncertainty.
Workflow assurance
Ranks fifteen criteria across twelve dataset-lifecycle stages and links requirements to evidence dimensions.
Evidence package
Retains CSV, JSON, figures, source annotations, tests and editable documentation.
Methodology
Project workflow
- 01Declare the purpose
Identify the decision, affected population, operating context and consequences of error.
- 02Trace the sources
Record acquisition, original purpose, permissions, immutable raw versions and responsible parties.
- 03Record transformations
Link cleaning, annotation, splitting and packaging steps to exact inputs, code, parameters and outputs.
- 04Measure fitness
Evaluate quality, representation, annotation and leakage for the intended use.
- 05Review the gates
Inspect failed evidence requirements, uncertainty and the highest workflow risks.
- 06Control change
Version the release and document monitoring, correction, deletion and withdrawal decisions.
Demonstration scenario
Run the complete study, compare a narrative dataset card with a W3C provenance graph and the lifecycle-wide framework, inspect where hard gates change the result, then trace the balanced leader through uncertainty, sensitivity and the highest-priority evidence gaps.
Engineering
Tools and method
- Evidence model
- Python structures define every framework, dimension, risk, context, criterion and assurance requirement.
- Numerical assessment
- NumPy and pandas implement deterministic cases, uncertainty, sensitivity, FMEA and assurance tables.
- Provenance standards
- The report maps W3C PROV, DCAT, DQV, dataset cards and artifact manifests into one evidence architecture.
- Documentation
- The build creates editable Word and fixed PDF report and guide files with contents, figure and table lists.
- Reproducibility
- Pinned dependencies, a fixed seed, automated tests, repository validation and Docker repeat the retained study.
Testing
Evaluation
Evaluation measures
- Eight frameworks across twelve evidence dimensions
- Seven hundred sixty-eight deterministic assessment cells
- Six explicit origin, rights, lineage, quality, bias and drift gates
- Four balanced, rights, quality and traceability scenarios
- Twenty thousand uncertainty draws per framework
- One hundred eighty workflow FMEA cells and one hundred twenty assurance links
- Ten automated tests and twelve reproducible figures
Project boundaries
- All zero-to-ten values are literature-informed screening positions, not measurements from a named dataset or organization.
- The project contains no personal data, source dataset, restricted evidence or buyer information.
- Documentation and provenance do not prove lawful collection, empirical data quality or absence of bias.
- The study is not legal advice, compliance certification or approval to deploy a high-risk AI system.
- A real implementation requires source-specific domain, rights, privacy, quality and governance review.
- No information collected.
Included
- 01Eight dataset-assurance framework archetypes
- 02Twelve source, rights, lineage, quality, bias and lifecycle dimensions
- 03Six risk families and four high-risk AI contexts
- 04768 deterministic assessment cases
- 0520,000 fixed-seed uncertainty draws per framework
- 06Four decision scenarios, fifteen validation criteria and workflow FMEA
- 07Twelve generated analytical figures and one sourced W3C literature figure
- 08Ten automated tests and clean Docker reproduction
- 09Complete project files, calculations, results and analysis in a private GitHub repository
- 1086-page project documentation in PDF and editable Word formats
- 1116-page setup and usage guide in PDF and editable Word formats
- 1265 annotated references with a complete source matrix
Project record
No information is collected on this page.
- Permanent project ID
- GP-DA-1JFH688
- Catalogued
- 21 Aug 2026
- Completed
- 04 Sept 2026
- Verified
- 04 Sept 2026
- Demonstration
- Included in repository
Handover
After purchase
- 01Payment is confirmed
The project is marked unavailable and cannot be purchased again.
- 02Repository access is granted
The buyer's submitted GitHub account receives access to the private repository.
- 03The purchase record is delivered
The certification sheet is prepared from the reviewed buyer details and sent privately by email.