Agentic data science with the Data Agent Kit
cloud.google.com·8h ago
AI Research
arXiv CS.AI·25d ago·5 min read
Who Audits Whom, on What Substrate, with What Evidence? An Independence-Graded Audit Protocol for Agentic AI
TL;DR
An independence-graded audit protocol for agentic AI systems was developed, assessing auditor independence across three axes: principal, substrate, and evidence. This approach enhances the reliability of audits by identifying the weakest link in the auditing process.
✦ Why It Matters
Implement an independence-graded audit protocol to enhance the reliability of your AI system evaluations today.
Key Takeaways
- Adopt a graded approach to auditor independence in AI systems.
- Utilize the seven-step protocol for third-party verification.
- Assess auditor independence across principal, substrate, and evidence axes.
- Align auditing practices with EU AI Act and ISO/IEC standards.
How It Works
The independence-graded audit protocol evaluates auditor independence on three axes: principal independence assesses who controls the auditor, substrate independence checks for shared foundations between auditor and auditee, and evidence independence distinguishes between attestable and self-reported evidence. By grading these axes, the protocol identifies the weakest link in the auditing process, enhancing overall reliability.