Aviation Safety Model for AI Risk Management

Aviation’s proven safety culture offers a roadmap for the AI industry to manage systemic risk.

UND (University of North Dakota) aviation safety expert James Higgins argues that the commercial airline sector’s long-running practices in aviation safety—transparent reporting, cross-operator learning and independent investigation—could help AI companies build stronger, more reliable systems. Higgins points to aviation’s decades-long decline in accidents driven by industry-wide data sharing and standard hazard analysis.

Higgins calls for AI developers, platforms and regulators to adopt similar norms: routine, non-punitive incident reporting; centralized analysis that identifies root causes across different systems; and public lessons that raise the baseline safety of the whole sector. The proposal is framed not as regulatory mimicry but as cultural transfer: aviation safety grew from operators cooperating to prevent repeat failures.

Aviation safety lessons for AI firms

Key elements that make aviation safety effective include consistent reporting standards, independent investigations, and a feedback loop that turns findings into design or procedural changes. For airlines and manufacturers, these practices reduced common-cause failures across fleets and models. Higgins suggests AI could benefit the same way when companies share anonymized failure data, modeling errors, and near-miss events.

• Establish non-punitive, standardized reporting channels for incidents and near-misses—mirroring aviation safety reporting—to surface systemic AI risks.
• Create independent analysis bodies that aggregate reports, look for cross-platform patterns, and publish actionable recommendations.
• Adopt industry-wide checklists, testing protocols and post-incident corrective measures so operators can learn from peers.

Adapting aviation-style transparency won’t be straightforward. AI firms face intellectual property, competition and national-security concerns that airlines typically do not. Still, Higgins argues limited, anonymized sharing and trusted intermediaries could strike a balance between commercial secrecy and collective safety gains. The goal is reducing repeatable failures and improving public trust in complex systems.

Whether regulators, consortia or independent non-profits lead the effort, the core idea is simple: treat AI system failures as shared lessons rather than isolated embarrassments. Aviation’s record shows that industries can lower risk faster when they learn together.