CTO, testAIng.com In recent past there has been a spate of accidents involving AI and Machine learning models in practice and deployment. Much so that there is an active database of all such accidents being chronicled (https://incidentdatabase.ai/ ). At a time when AI is making strides in radical

The current notions of quality assurance and testing in AI/ML pipelines is based on the idea of validation using a random set-aside set of data on which the model is tested and metrics computed thereof. Metrics like accuracy on the random set-aside data set termed ambiguously as test data,

In today s world of machine learning dominated Artificial Intelligence applications, there is a renewed push for the agenda of explainability. The triggers for explainability could be multifold: * A comfort feeling of knowing what you are handing over control to * A knowledge of the influence of the precise input component