OpenChainGraph Suite · ART-452 · Banking / AI Data Governance

AI Training-Data Lineage Record

Composes a hash-chained ML training-data lineage record — dataset identity, upstream source datasets, a declared collection/governance method, and an OPTIONAL reference to an existing OCG receipt for the training-run compute (tool identity, execution hash, kernel digest — never re-embedded).

Data Governance EU AI Act Art 10 / Annex IV 2(d) SR 26-2 Data Lineage (formerly SR 11-7)
Supersession notice (added 2026-08-02): On 17 April 2026 the Federal Reserve issued SR 26-2, Revised Guidance on Model Risk Management, which supersedes and replaces SR 11-7. Any reference to SR 11-7 on this page describes the framework as it stood before that date.
🔒 All inputs are processed locally in your browser. No data is transmitted. Do not enter real personal data — use synthetic or anonymised inputs only.
⚠ dataset_id and source_dataset_ids are STRUCTURAL catalog identifiers only. This kernel never ingests, stores, or echoes dataset contents. It documents dataset lineage and governance metadata — it does NOT validate dataset quality, bias, or representativeness.
Not build_ai_decision_log_record (art-236, per-inference decision chain) and not build_ai_workpaper_record (art-380, audit workpaper over a receipt). All three may reference the same underlying receipt hash without re-embedding it.
Dataset Identity
Referenced Training-Run Receipt (optional)
Chain + Operator
Lineage Record Detail
Execution Hash (SHA-256)







  

    
Ask your agent

    
  

  
Copy this paragraph into Claude, OpenClaw, or any MCP-aware agent to run this exact tool, with this sample, and verify the artifact.

  
Run the AINumbers MCP tool `build_ai_training_data_lineage_record`. Task: Compose a hash-chained ML training-data lineage record: dataset identity, dataset version, upstream source dataset references, a declared collection/governance method, and an OPTIONAL reference to an existing OCG receipt for the training-run compute (tool identity, execution hash, kernel digest -- never re-embedded).
Synthetic sample input (policy_parameters): {"dataset_id":"ds-credit-decisioning-2026q2","dataset_version":"1.0.0","source_dataset_ids":["ds-core-banking-txn-log-2024-2026"],"collection_method":"internal_transaction_records","governance_notes":"Sourced from internal core-banking transaction extracts, deduplicated and de-identified prior to model training per firm data-governance policy.","referenced_receipt_tool_id":"","referenced_receipt_tool_version":"","referenced_receipt_execution_hash":"","referenced_receipt_kernel_digest":"","sha256_prev_lineage_hash":"","retention_months":12,"operator_id":"mrm-data-governance-team"}
Verify before trusting: call `verify_execution_hash` on mcp.ainumbers.co (https://mcp.ainumbers.co/mcp) with the returned execution_hash.
Return the ledger link https://ledger.ainumbers.co/ so a human can re-verify without contacting us.
PII rule: All inputs are processed locally in your browser. No data is transmitted. Do not enter real personal data — use synthetic or anonymised inputs only.
Open the tool with the sample prefilled: https://ainumbers.co/chaingraph/art-452-build-ai-training-data-lineage-record.html#p=v1.H4sIAAAAAAAA_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