← Back to Glossary
Auditability in AI Systems
Definition
Auditability in AI systems is the ability to inspect, trace, and verify how an AI system produced a specific output, decision, or action based on its inputs, context, and configuration.
Purpose
The purpose of auditability in AI systems is to enable accountability, regulatory compliance, incident investigation, and trust by making system behavior observable and verifiable after execution.
Key Characteristics
- Traceability of inputs, context, and outputs for each inference or action
- Logging of model versions, prompts, tools, and data sources involved in execution
- Ability to reconstruct decision paths and execution flows
- Support for internal reviews, external audits, and regulatory reporting
- Separation between operational logs and audit-relevant records
Usage in Practice
In practice, auditability is used to investigate incidents, demonstrate compliance with regulations, analyze model behavior, and provide evidence of how and why specific AI-driven decisions were made.
One implementation of this concept is offered by Kenaz through the AI Safety & Compliance Audit service.