The paper introduces RoMA and gRoMA as statistical tools that compute auditable upper bounds on the failure probability of any black-box AI system once regulators fix an acceptable risk threshold and input domain.
Anderson-Darling Tests of Goodness- of-Fit
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
fields
cs.AI 1years
2026 1verdicts
UNVERDICTED 1representative citing papers
citing papers explorer
-
Bounding the Black Box: A Statistical Certification Framework for AI Risk Regulation
The paper introduces RoMA and gRoMA as statistical tools that compute auditable upper bounds on the failure probability of any black-box AI system once regulators fix an acceptable risk threshold and input domain.