Executive Summary
This report publishes a Decision Traceability field framework and a completeness measurement design for reconstructing AI-assisted decisions. Publication date: 2026-09-10. Last updated: 2026-09-10.
Completeness rates and field-fill rates are not published. Measurement not yet available; framework published; completeness benchmark forthcoming.
Related research: human override study, evaluator methodology, workflow performance. Authority: what to record, audit trail for LLMs and agents, and Decision Ledger.
Key Findings
No completeness percentages are claimed.
- A measured public completeness rate is not yet available from the corporate evidence corpus.
- Decision Ledger field design specifies the artifacts required for reconstruction of authorization and provenance.
- NIST SP 800-92 provides foundational log-management expectations that inform AI decision audit design[2].
- NIST AI RMF and federal AI governance memoranda treat traceability and oversight as governance obligations[1][3].
- CISA AI guidance reinforces operational security and resilience context for recording consequential automated actions[4].
Research Question
Which fields must be present for an AI-assisted decision to be reconstructable, and what share of ledger records meet that completeness bar under a fixed scoring rubric?
Completeness share: measurement not yet available.
Dataset and Measurement Status
Dataset size: Not published — measured corpus not yet released.
Methodology document ID: SL-RG-METHOD-2026.1 (methodology only — not a dataset).
Status: framework published; completeness benchmark forthcoming.
Methodology
Define a required-field checklist for consequential decisions. Score each ledger record as complete, partial, or non-reconstructable. Publish rates only after a measured sample is released under SL-RG-METHOD-2026.1.
Until release: measurement not yet available for completeness benchmarks.
Decision Traceability Field Checklist
Field list for measurement design — not a scored sample.
| Field group | Example elements | Why it matters |
|---|---|---|
| Model provenance | Provider, model family, identifier, version, route | Explains which system produced the output |
| Input context | Prompt or task ID, retrieval sources or document IDs (as policy allows) | Supports reconstruction of what the model saw |
| Comparison artifacts | Alternate model outputs retained for review | Needed when disagreement informed the decision |
| Human action | Reviewer identity, action class, rationale | Separates model suggestion from institutional action |
| Authorization | Authorizer, timestamp, exception flags | Establishes accountability for the decision |
| Retention metadata | Record ID, integrity/export markers per policy | Supports audit export and log management practice |
Source: Smart Logic AI Decision Ledger field design and research methodology SL-RG-METHOD-2026.1; measured results not yet published.
Management Implications
Treat incomplete records as a control failure for high-consequence use cases, not as a reporting inconvenience.
Align checklist requirements with audit trail guidance and ledger field guidance before instrumenting rates.
Pair with override study design so human actions are not orphaned from provenance: override study.
Limitations
No measured completeness sample is released. Field presence rates cannot be stated.
Privacy and classification rules may legitimately redact input context; completeness scoring must account for permitted redaction without inventing fill rates.
External citations inform logging and governance practice; they are not Smart Logic AI completeness scores[1][2][3][4].
How Smart Logic Approaches This
SmartSolo is built around Decision Ledger evidence for governed multi-model decisions — provenance, review, and authorization as reconstructable artifacts.
Research will publish completeness benchmarks only after measured ledger samples meet SL-RG-METHOD-2026.1.
See Decision Ledger and the evaluator methodology.
References
Authoritative sources cited for standards and methodology claims. Inline markers link here.
Citation kit
Report title: From AI Output to Defensible Decision: Benchmarking Decision Traceability
Publisher: Smart Logic AI
Publication date: 2026-09-10
Last updated: 2026-09-10
Canonical URL: https://www.smartlogicusa.com/research/ai-decision-traceability-benchmark
Methodology URL: https://www.smartlogicusa.com/research/multi-model-evaluator-methodology
Dataset / version identifier: Not assigned — measured corpus not yet published
Method document ID: SL-RG-METHOD-2026.1
Data coverage: Measured public corpus not yet released; methodology coverage begins 2026-09-10.
Suggested citation: Smart Logic AI. “From AI Output to Defensible Decision: Benchmarking Decision Traceability.” 2026. https://www.smartlogicusa.com/research/ai-decision-traceability-benchmark.
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