eticas-829b71b4·1 events·first seen Aliases: Eticas
Eticas presents a structured AI auditing framework that bridges risk cataloging to executable audit methodology, demonstrated end-to-end on PII leakage testing against GPT-4-0314. The taxonomy organizes 76 active subcategories across 10 categories with mappings to 18 external frameworks, and is published under CC BY 4.0 with SKOS/JSON-LD distributions. The key contribution is an operationalization layer that converts named risks into measurable, severity-graded findings — addressing a gap the authors identify across at least 74 existing AI risk taxonomies. The PII leakage demonstration shows disclosure rates ranging from 0% to 84% under adversarial conditioning, graded as SYSTEMIC severity.