Executive Summary
This report defines a Decision Ledger–based study of human review, modification, override, rejection, and escalation of AI-assisted outputs. Publication date: 2026-09-10. Last updated: 2026-09-10.
Measured override rates, modification rates, and escalation rates are not published. Measurement not yet available; study design published; measured Decision Ledger results forthcoming.
Cross-links: decision traceability benchmark, governed workflow performance, agreement/divergence. Authority: human-in-the-loop AI, Decision Ledger fields, and AI governance platform.
Key Findings
No measured human–AI interaction rates are claimed.
- A measured public override rate is not yet available from the corporate evidence corpus.
- Decision Ledger field design specifies the artifacts required to reconstruct review, override, and authorization.
- Human oversight expectations in NIST AI RMF and federal AI governance memoranda motivate retaining who acted and why[1][2][3].
- OECD AI Principles reinforce human accountability for AI-assisted decisions[4].
- Override without a retained reason is an evidence gap, even when the final decision was correct.
Research Question
When humans review AI-assisted outputs in a governed workflow, how often do they accept, modify, override, reject, or escalate — and what ledger fields make those actions reconstructable?
Quantitative frequencies: measurement not yet available.
Dataset and Measurement Status
Dataset size: Not published — measured corpus not yet released (no public Decision Ledger event corpus released).
Methodology document ID: SL-RG-METHOD-2026.1 (methodology only — not a dataset).
Status: study design published; measured results forthcoming.
Methodology
Unit of analysis is a Decision Ledger event with model provenance, human actor identity, action type, timestamp, and rationale fields as specified in field design. Events without required fields are incomplete and excluded from rate numerators until completeness rules are met — see traceability benchmark.
Action taxonomy is fixed before measurement. No rates are computed in this publication.
Human Action Classes for Ledger Study
Taxonomy for future measurement — not observed rates.
| Action class | Definition | Minimum ledger fields |
|---|---|---|
| Accept | Human authorizes the AI output without material change | Actor, timestamp, model provenance, authorization |
| Modify | Human edits content before authorization | Actor, before/after or edit summary, authorization |
| Override | Human selects a different recommendation than the primary model path | Actor, discarded path, chosen path, reason |
| Reject | Human declines the AI output without authorizing an alternative from that run | Actor, reason, next step |
| Escalate | Human routes to higher authority or additional review | Actor, escalation target, reason, timestamps |
Source: Smart Logic AI Decision Ledger field design and research methodology SL-RG-METHOD-2026.1; measured results not yet published.
Management Implications
Define authority thresholds before collecting override telemetry. Without an authority matrix, “override rate” lacks meaning — see human-in-the-loop program design.
Pair override study design with workflow cycle-time measurement only when both use the same ledger events: governed workflow performance.
Do not substitute marketing anecdotes for ledger-derived frequencies.
Limitations
No public event rows are released. Any numeric override claim would be unsupported.
Incomplete ledger records bias future rates toward better-instrumented teams; completeness gating is required before publication.
Cited sources set oversight expectations; they do not report Smart Logic AI override statistics[1][2][3][4].
How Smart Logic Approaches This
SmartSolo structures human review gates and Decision Ledger retention so accept, modify, override, reject, and escalate can be studied when a measured corpus exists.
Until then, Smart Logic AI publishes field design and methodology only under SL-RG-METHOD-2026.1.
Related: Decision Ledger and traceability.
References
Authoritative sources cited for standards and methodology claims. Inline markers link here.
Citation kit
Report title: When Humans Override AI: What Governed Review Reveals About Model Reliability
Publisher: Smart Logic AI
Publication date: 2026-09-10
Last updated: 2026-09-10
Canonical URL: https://www.smartlogicusa.com/research/human-ai-override-study
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. “When Humans Override AI: What Governed Review Reveals About Model Reliability.” 2026. https://www.smartlogicusa.com/research/human-ai-override-study.
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