Incident Response for AI
Procedures for identifying, containing, and resolving failures or harmful behaviors in deployed AI systems. AI incident response adapts traditional IT incident management for AI-specific challenges.
Why It Matters
AI incidents can escalate from a single bad prediction to a PR crisis in hours. Having a response plan before incidents occur is critical.
Example
When a chatbot starts giving harmful medical advice: immediate takedown of the affected feature, root cause analysis, model rollback, stakeholder notification, and post-mortem review.
Think of it like...
Like a fire drill for AI failures — when something goes wrong, everyone knows their role, the containment steps, and the communication plan.
Related Terms
AI Governance
The frameworks, policies, processes, and organizational structures that guide the responsible development, deployment, and monitoring of AI systems within organizations and across society.
Model Monitoring
The practice of continuously tracking an ML model's performance, predictions, and input data in production to detect degradation, drift, or anomalies after deployment.
AI Safety
The research field focused on ensuring AI systems operate reliably, predictably, and without causing unintended harm. It spans from technical robustness to long-term existential risk concerns.
Deployment
The process of making a trained ML model available for use in production applications. Deployment involves packaging the model, setting up serving infrastructure, and establishing monitoring.