Human in the Loop AI Governance for eAIP: Balancing Generative Models with ICAO Annex 15

<p>Artificial intelligence is changing how aeronautical information is created validated and distributed. Civil Aviation Authorities can gain productivity and quality improvements from generative models that draft procedure text translate notices and assist with anomaly detection. At the same time aviation regulators and operational users require auditable provenance predictable effective dates and reliable human oversight. This article explains a practical governance approach that keeps humans in the loop while unlocking AI value for electronic AIP publishing.</p><p>Start from the data backbone. Modern eAIP platforms provide structured machine readable content that AI can consume and improve on. When content is organized as discrete modules with clear metadata such as effective dates approval status and author identity AI can suggest edits or draft paragraphs without replacing editorial control. The authoritative repository remains the single source of truth and every AI suggestion is treated as an editorial proposal that requires named human approval before publication.</p><p>Combine deterministic validation with probabilistic checks. ICAO Annex 15 style validations catch format errors coordinate mistakes and missing fields. AI models complement those checks by detecting subtle anomalies and by proposing corrections based on historical edits and external reference datasets. Use both types of checks during authoring so issues are detected early and human reviewers focus on items that need operational judgement rather than routine fixes.</p><p>Design clear human review gates. Define which changes AI may approve automatically and which always require manual sign off. Low risk tasks such as language translation standard paragraph generation and format normalisation are good candidates for semi automated handling. High impact changes such as procedure geometry amendments facility frequencies and AIRAC bound edits must maintain strict human approval with recorded rationale. The review process should be fast but unambiguous so responsibility is traceable for auditors and operators.</p><p>Preserve effective date discipline. AIRAC cycles and non AIRAC updates coexist in modern operations. Treat AI assisted drafts as draft work products. Tag every suggested change with metadata that indicates whether it is intended for the next AIRAC or for immediate operational release. Provide a sandbox feed for downstream consumers to validate ingest and to remove last minute surprises. On the effective date publish signed snapshots that downstream systems can verify.</p><p>Record provenance and maintain immutable audit logs. Every AI interaction should be logged including the model version prompt and the user action that accepted rejected or modified the suggestion. Use version control so each commit includes approver identity timestamps and a short explanation of the change. When auditors or safety teams need to inspect a decision they must be able to reconstruct how a procedure or a NOTAM evolved from draft to publication.</p><p>Deploy responsible model management. Keep model training and prompt design under configuration control. Use a test dataset derived from prior approved AIP work to evaluate model accuracy and bias. Establish a cadence for model review and retraining and record model versions used for production suggestions. When a model is updated keep a rollback option so any regression can be mitigated quickly.</p><p>Automate integration with NOTAM and downstream systems. AI assisted drafting often reduces time to prepare a NOTAM or a short amendment. Link NOTAM creation to the authoritative AIP module so temporary messages do not contradict published content. Expose webhook notifications and sandbox feeds for airline operation centers navigation database suppliers and charting providers so they can validate AI assisted changes ahead of the effective date.</p><p>Include cybersecurity and data governance controls. Use role based access control multifactor authentication and tenant isolation when models process sensitive or sovereign data. Ensure that API keys and service accounts use short lived credentials and that all payloads are transported with strong encryption. Maintain secure storage for model prompts training data and for any keys used to sign published artifacts.</p><p>Measure impact with clear KPIs. Track time saved in drafting and translation reduction in post publication corrections validation pass rates and percent of downstream consumers on authoritative API feeds. Monitor user confidence and error trends to refine the model and the workflow rules. Start conservative and scale as the governance framework proves robust.</p><p>How FlyClim can help. The FlyClim eAIP platform provides the structured repository validation engines AIRAC automation and version control that are required to adopt AI responsibly. We integrate AI features as assistive tools not as autonomous publishers. Practical ways FlyClim supports adoption include configurable approval workflows that keep human oversight in the loop automated provenance logging and signed export artifacts for each AIRAC cycle. Our sandbox and API first distribution let downstream consumers validate changes before they are effective. For authorities that need it we offer consulting services to design pilot programs to test AI assisted drafting translation and anomaly detection while preserving Annex 15 compliance and auditability.</p><p>Implementation roadmap. Begin with a focused pilot on low risk content such as GEN section text or standard advisory paragraphs. Configure validation rules and a human review gate. Expose a sandbox feed to one airline or one navigation database supplier. Measure drafting time savings and error rates. Iterate on prompts validation thresholds and approval rules then expand to higher impact modules as confidence grows.</p><p>Conclusion. Generative AI can materially improve productivity and data quality in aeronautical information management when deployed with strong governance human oversight and a validated data backbone. Civil Aviation Authorities that adopt a human in the loop approach keep control over authoritative publications while benefiting from automation. FlyClim combines an eAIP platform built for Annex 15 compliance with practical consulting to help authorities run safe measurable pilots and scale AI responsibly.</p><p>Learn more and request a pilot at https://eaip.flyclim.com and https://flyclim.com or contact Davide at davide@flyclim.com for a direct discussion.</p>