Eticas AI Risk Taxonomy v2.0.0: Open Infrastructure for Operationalizing AI Audits
A new preprint introduces the Eticas AI Risk Taxonomy v2.0.0, an open infrastructure designed to operationalize AI audits by connecting risk catalogs to executable, measurable tests. The taxonomy organizes 76 active subcategories across 10 categories, mapped to 18 external frameworks, and demonstrates its approach with an end-to-end example of PII leakage risk assessment on GPT-4-0314. The framework is published as open semantic infrastructure, providing a standardized method for translating risk identification into graded audit findings.
Why it matters: This work addresses a critical gap in AI auditing by providing a practical, open, and standardized bridge from risk identification to measurable, actionable audit results.
Full story at: arXiv Computers and Society ↗