Prediction Integrity · AI Forecast Audit
AI Forecast Audit Trails
Trace every AI forecast from input data through model version, generation, approval, publication, and outcome. CertifiedData proves the artifacts. DecisionLedger proves the events between them.
Forecast accountability depends on lineage. Without it, every forecast is an isolated output. With it, the full path from data to decision is verifiable by any party — without trusting the platform that produced it.
Why forecast lineage matters
Four structural reasons. Each holds independently — together they form the case for treating forecast lineage as infrastructure, not paperwork.
Accountability requires upstream proof
When a forecast influences a decision — investment, treatment, hiring, settlement — accountability for that decision depends on being able to trace the forecast back to its inputs. Lineage is what makes the trace possible. Without it, every forecast is an isolated output.
Model evaluations need stable references
Studies comparing model performance need to reference specific model versions, specific training datasets, and specific input contexts. Certificate IDs are stable, portable references that survive paper review cycles, library refactors, and platform migrations.
Regulatory audit follows the chain
EU AI Act Article 11 (technical documentation), Article 12 (logging), and Article 19 (technical documentation) all require traceability between AI inputs, outputs, and decisions. A certified lineage chain produces machine-verifiable documentation that satisfies multiple articles as a side effect of normal operation.
Post-incident review reconstructs the past
When something goes wrong — a bad forecast, an unexpected outcome, a regulatory inquiry — the first task is to reconstruct what happened. A complete lineage chain gives the reviewer a verifiable starting point that does not depend on memory, screenshots, or platform good faith.
Lifecycle stages
Eight stages. Each one produces a verifiable record. CertifiedData stages produce signed certificates; DecisionLedger stages produce chain-linked events.
The forecast pipeline reads from a dataset — internal records, third-party feeds, or certified synthetic data. The dataset certificate ID is captured at read time and carried through the lifecycle as the data_certificate_id reference.
The pipeline selects a model artifact — a specific weights file with its own certificate. The model_certificate_id is captured. If the model artifact references a training data certificate, that link is included transitively.
The model produces a forecast given the input context. The forecast payload is canonicalized, hashed, and signed — producing the prediction_receipt.v1 certificate that anchors this forecast in the audit trail.
If the model produces a confidence score, a calibration bucket, or a brief rationale, those values are captured in the signed forecast payload. This makes the model's stated uncertainty part of the tamper-evident record.
Forecasts that require human or system approval before publication trigger an approval event. The approver identity, decision (approved / rejected / escalated), and timestamp are appended to the Decision Ledger chain.
When the forecast is published (to a website, an API, an analytics product, a partner feed), the publication event is logged. Where it was published, when, and under which version of the surface — all in the decision chain.
When the predicted event resolves, the resolution_record.v1 certificate is issued (see Resolution Audit Trails). The resolution certificate references the prediction manifest, closing the loop between forecast and outcome.
Aggregate accuracy metrics — hit rate, calibration, Brier score, profit-and-loss — are computed offline and logged as Decision Ledger metric events. These are reporting events, not proof events; the proof lives in the upstream certificates.
How the linkage works
Every certificate carries a reference to its upstream artifacts. Every decision event carries a reference to the certificate it relates to. The result is a navigable graph from any forecast back to its inputs.
Training data cert Forecast cert
│ │
│ │
▼ ▼
Model artifact ─────► Prediction
cert receipt
│ │
│ └─► Decision Ledger:
│ approval event
│ publication event
│ outcome event
│ accuracy event
▼
data_certificate_id, model_certificate_id, and decision events
all link back through the audit trailCertifiedData + DecisionLedger split
CertifiedData proves the artifacts. DecisionLedger proves the events. The split is not arbitrary — artifacts are stateful things that exist; events are actions that happened.
- →Input data certificates (training_data.v1)
- →Model artifact certificates (model_artifact.v1)
- →Prediction receipts (prediction_receipt.v1)
- →Daily prediction manifests (prediction_manifest.v1)
- →Resolution records (resolution_record.v1)
- →Forecast generation event
- →Approval event (approve / reject / escalate)
- →Publication event
- →Resolution action event
- →Accuracy metric event
Who builds on AI forecast audit trails
| Role | What they need |
|---|---|
| AI governance leaders | End-to-end forecast traceability across the AI supply chain. Reviewable evidence for board, audit committee, and regulator briefings. |
| Enterprise compliance teams | EU AI Act Article 10/11/12/19 evidence in a single linked chain. Exportable audit packs for incident response and regulator requests. |
| Model evaluators and researchers | Stable references to model versions, training datasets, and prediction archives. Replicable benchmarks for AI forecasting studies. |
| Internal audit and risk teams | Independent verification of forecast inputs, model versions, and approval events. Audit trails that do not require platform access. |
| Insurance and financial review | Forecast lineage for risk-bearing decisions — credit, underwriting, treatment recommendations. Discoverable evidence for dispute resolution. |
| Sales and customer success | Customer-facing 'how this forecast was made' surfaces. The chain becomes a trust signal in the buying process. |
Machine-readable summary
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}Build the full forecast audit trail
Combine certified data, model, prediction, and resolution artifacts with Decision Ledger events. The chain becomes the trust infrastructure for the entire forecast lifecycle.