Human Oversight Procedure Playbook (Article 14 compliant)
Download an Article 14 compliant human oversight playbook covering roles, approval workflows, queue management, override procedures, training requirements, and evidence capture.
Define a reviewable human oversight SOP in 30-40 minutes.
For compliance, risk, product, and ML ops teams shipping agentic workflows into regulated environments.
Ultimo aggiornamento: 16 dic 2025 · Versione v1.0 · Campione fittizio. Non costituisce consulenza legale.
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Cos'è questo artefatto (e quando vi serve)
Spiegazione essenziale minima, scritta per gli audit, non per la teoria.
EU AI Act Article 14 requires high-risk AI systems to be designed for effective oversight by natural persons. This playbook translates Article 14 requirements into operational procedures.
It covers 7 parts: Article 14 requirements, oversight roles, approval workflows, queue management, override procedures, training requirements, and evidence capture.
Vi serve quando
- You are inserting approval gates into AI workflows (high-risk actions, sensitive data access, production changes).
- You need to prove who approved/overrode a decision and what context they saw.
- You are preparing a human oversight section for Annex IV or an internal control review.
Common failure mode
"Human in the loop" described in prose with no role authority matrix, no queue SLAs, no override documentation requirements, and no training competency verification.
Com'è fatto un buon risultato
Criteri di accettazione che i revisori verificano effettivamente.
- Roles and authority levels are explicit with competency requirements and recertification schedules.
- Approval workflows define triggers, steps, and documentation for standard, escalation, and exception paths.
- Queue management includes SLAs, prioritization rules, workload distribution, and bottleneck response.
- Override procedures cover pre-execution, post-execution reversal, and emergency overrides with reason categories.
- Training program defines curriculum, role-specific requirements, and ongoing competency verification.
- Evidence capture specifies data points, capture methods, and integrity requirements for all oversight actions.
Anteprima del template
Un estratto reale in HTML così è indicizzabile e revisionabile.
## Part 2: Oversight Roles and Responsibilities ### 2.1 Oversight Role Definitions | Role | Authority Level | Training Required | |------|-----------------|-------------------| | AI Operator | Level 1: Standard decisions | Initial cert + annual refresh | | Senior Operator | Level 2: Elevated decisions | Advanced cert + quarterly review | | Oversight Supervisor | Level 3: Override authority | Supervisory cert + monthly calibration | ## Part 5: Override and Reversal Procedures ### 5.1 Override Types | Override Type | Authority Required | Documentation Level | |---------------|-------------------|---------------------| | Pre-execution override | Standard approval authority | Standard | | Post-execution reversal | Level 2+ | Enhanced with justification | | Emergency override | Any trained operator | Emergency protocol + retrospective |
Come compilarlo (rapidamente)
Input necessari, tempo di completamento e un esempio pratico in miniatura.
Input necessari
- Role definitions with authority levels and competency requirements.
- Approval workflow triggers, steps, and documentation requirements.
- Queue SLAs, prioritization rules, and bottleneck response procedures.
- Override types, authority requirements, and reason categories.
- Training curriculum with role-specific requirements and recertification.
- Evidence data points, capture methods, and integrity requirements.
Tempo di completamento: 30-40 minutes for v1, then iterate with real review logs.
Mini example: override reason categories
Override Reason Categories: | Category | Description | Review Priority | |----------|-------------|-----------------| | SAFETY | Safety concern for user or third party | Immediate review | | ERROR | Obvious AI error or malfunction | High priority | | CONTEXT | AI lacked relevant context | Standard review | | POLICY | Policy consideration AI cannot assess | Standard review | | JUDGMENT | Human judgment differs on edge case | Pattern analysis |
Come KLA lo genera (Governare / Misurare / Dimostrare)
Collegate l'artefatto alle primitive di prodotto per favorire la conversione.
Govern
- Policy-as-code checkpoints that block or require review for high-risk actions.
- Versioned change control for model/prompt/policy/workflow updates.
Measure
- Risk-tiered sampling reviews (baseline + burst during incidents or after changes).
- Near-miss tracking (blocked / nearly blocked steps) as a measurable control signal.
Prove
- Hash-chained, append-only audit ledger with 7+ year retention language where required.
- Evidence Room export bundles (manifest + checksums) so auditors can verify independently.
FAQ
Scritte per ottenere risposte in formato snippet.