As AI systems are increasingly used in critical areas like healthcare, finance, and transportation, their failures can cause serious harm. Unlike industries such as aviation—where incident reporting prevents repeated accidents—AI lacks a structured way to track failures, near-misses, or harmful outcomes. Without clear reporting rules, regulators and developers miss opportunities to improve safety, enforce accountability, or spot systemic risks before they escalate.

How an AI Incident Reporting System Could Work

One approach to addressing this gap could involve creating mandatory incident reporting requirements for high-risk AI applications, inspired by frameworks in aviation, cybersecurity, and industrial safety. Key features might include:

• Targeted Mandates: Critical AI systems (e.g., medical diagnostics, autonomous vehicles) could be legally required to report incidents where harm occurred—or was narrowly avoided—due to unexpected behavior.
• Dual Reporting Tiers: Some reports could remain confidential to encourage transparency without exposing companies to reputational risk, while severe or repeated issues might be disclosed publicly.
• Standardized Formats: A consistent template could help regulators analyze incidents efficiently, covering details like system inputs, failure modes, and corrective actions.

Potential beneficiaries could span regulators (gaining visibility into risks), developers (spotting flaws early), and the public (experiencing fewer harmful errors). Researchers might also access anonymized data to study failure patterns.

Implementation Strategies and Comparisons

A phased rollout could start with a voluntary reporting system, offering liability exemptions or other incentives to encourage participation. Over time, this could transition to mandatory rules backed by penalties for non-compliance. Key steps might involve:

1. Piloting the system in a high-stakes sector (e.g., healthcare AI) to refine thresholds and reporting processes.
2. Creating a regulatory body to analyze trends and propose safety upgrades.
3. Launching a public database for non-sensitive incidents, similar to the FAA’s aviation safety repository.

Compared to existing models like the EU’s GDPR (focused on data breaches) or crowd-sourced AI failure databases, this approach could offer more comprehensive, standardized data—particularly for near-misses that might otherwise go undocumented.

Such a system could fill a critical gap in AI governance, moving from reactive damage control to proactive risk management. Challenges like underreporting might be mitigated by legal safeguards, while overreporting could be managed with clear severity thresholds.