SafeML: Safety Monitoring of Machine Learning Classifiers through Statistical Difference Measure

2 code implementations • 27 May 2020 • Koorosh Aslansefat, Ioannis Sorokos, Declan Whiting, Ramin Tavakoli Kolagari, Yiannis Papadopoulos

Ensuring safety and explainability of machine learning (ML) is a topic of increasing relevance as data-driven applications venture into safety-critical application domains, traditionally committed to high safety standards that are not satisfied with an exclusive testing approach of otherwise inaccessible black-box systems.

BIG-bench Machine Learning Domain Adaptation +5