What is AI Incident Reporting?

What is AI Incident Reporting?

AI Incident Reporting is the structured practice of documenting and responding to situations where AI systems fail, produce unintended outcomes, cause harm, or behave unexpectedly. It draws inspiration from incident reporting practices in industries such as aviation, healthcare, and cybersecurity, where systematic documentation of failures has proven essential for learning and improvement.

An AI incident can range from a model producing inaccurate predictions, to an algorithm making biased decisions that affect a specific group, to a complete system failure that disrupts operations. Near-misses, situations where harm was narrowly avoided, are equally important to document because they reveal vulnerabilities that could lead to actual incidents in the future.

The goal of AI incident reporting is not to assign blame but to create a systematic learning process that makes AI systems safer and more reliable over time.

Why AI Incident Reporting Matters

Learning from Failures

The most valuable lessons in AI often come from failures. When an AI system produces unexpected results, investigating the root cause reveals weaknesses in data, model design, testing, or operational processes that might otherwise remain hidden. Without a reporting system, these lessons are often lost as teams quietly fix problems without documenting what went wrong or why.

Regulatory Compliance

Regulators across Southeast Asia and globally are increasingly requiring organisations to document and report AI incidents. The EU AI Act mandates reporting of serious incidents for high-risk AI systems. Singapore's MAS has reporting requirements for AI failures in financial services. As AI governance frameworks mature across ASEAN, incident reporting requirements are expected to become more widespread and specific.

Risk Management

A pattern of similar incidents across different AI systems reveals systemic issues, perhaps in your data pipeline, testing practices, or model development methodology. Without systematic reporting, these patterns are invisible. With reporting, they become actionable insights that reduce future risk.

Stakeholder Accountability

When AI systems affect customers, employees, or the public, stakeholders expect accountability. Incident reporting demonstrates that your organisation takes AI failures seriously, investigates them thoroughly, and takes steps to prevent recurrence. This transparency builds trust and reduces the reputational damage associated with AI incidents.

Types of AI Incidents

Performance Failures

The AI system fails to perform its intended function with acceptable accuracy. A fraud detection model misses a wave of fraudulent transactions. A demand forecasting system produces wildly inaccurate predictions. A chatbot provides incorrect information to customers.

Bias and Discrimination

The AI system produces outcomes that disproportionately disadvantage specific groups. A hiring algorithm systematically ranks certain demographics lower. A credit scoring model denies loans at higher rates to specific ethnic groups. A facial recognition system misidentifies people of certain backgrounds at higher rates.

Privacy Violations

The AI system exposes, leaks, or misuses personal data. A recommendation system inadvertently reveals sensitive information about users. A language model reproduces personal data from its training set. An AI system shares data with unauthorised parties.

Safety Incidents

The AI system causes or contributes to physical harm or endangers safety. An autonomous vehicle makes an unsafe decision. A medical AI provides dangerous treatment recommendations. An industrial AI system creates hazardous operating conditions.

Security Breaches

The AI system is exploited through adversarial attacks, data poisoning, or other security vulnerabilities. An attacker manipulates a model's inputs to produce favourable outcomes. Training data is compromised. Model parameters or intellectual property are stolen.

Unexpected Behaviour

The AI system behaves in ways that were not anticipated during design or testing. A generative AI produces offensive content. A reinforcement learning system discovers an unintended strategy. An AI system interacts with another system in unexpected ways.

Building an AI Incident Reporting System

Define What Constitutes an Incident

Establish clear criteria for what should be reported. Too narrow a definition means important incidents go unrecorded. Too broad a definition creates noise that obscures significant events. Most organisations define tiers, such as critical incidents requiring immediate response, significant incidents requiring investigation, and minor incidents requiring documentation.

Create Accessible Reporting Channels

Make it easy for anyone in the organisation to report an AI incident or near-miss. This includes developers, operators, customer service representatives, and end users. Reporting should be as simple as possible and should not require extensive technical knowledge. Some organisations use dedicated reporting forms, while others integrate reporting into existing incident management systems.

Establish Investigation Processes

Define how reported incidents are investigated. For significant incidents, this typically involves a cross-functional team that examines the technical root cause, the process gaps that allowed the incident to occur, and the impact on affected individuals. The investigation should produce actionable recommendations, not just a technical explanation.

Document Thoroughly

Each incident report should include what happened, when it happened, who was affected, what the root cause was, what corrective actions were taken, and what preventive measures were implemented. This documentation serves multiple purposes: it supports learning, enables pattern analysis, and provides an audit trail for regulatory compliance.

Share Learnings

Incident findings should be shared across the organisation so that teams working on different AI systems can benefit from each other's experiences. This can take the form of internal incident reviews, shared databases of past incidents, or regular cross-team learning sessions.

Track Corrective Actions

Document the corrective actions taken in response to each incident and verify that they are implemented. Track whether similar incidents recur after corrective actions are put in place. This follow-through is what transforms incident reporting from documentation into genuine improvement.

AI Incident Reporting in Southeast Asia

The practice is gaining importance across the region. Singapore's approach to AI governance, which emphasises accountability and continuous improvement, naturally supports robust incident reporting. The MAS expects financial institutions to monitor and report on AI system performance, including failures and unexpected outcomes.

Globally, the AI Incident Database (AIID), a public repository of AI-related incidents, provides a growing body of evidence about the types of things that can go wrong with AI systems. Organisations in Southeast Asia can learn from this database while building their own internal reporting capabilities.

As ASEAN develops harmonised AI governance standards, incident reporting is expected to become a standard requirement, particularly for high-risk AI applications. Organisations that establish reporting practices now will be well-prepared for these requirements and will benefit from the learning and improvement these practices enable.