Introduction to Human In The Loop Machine Learning

Human In The Loop (HITL) is traditionally defined as "a model that requires human interaction" In a Machine Learning context, it implies a hybrid computation model whereby a human can overrule decisions taken by a machine where they are less likely to be correct. The point of the HITL model is to leverage the cost reduction benefits of automation – through algorithms, statistical models, AI, you name it – while mitigating potential errors by having people take over tricky situations. Most HITL systems assume high confidence algorithm-based decisions need no human intervention.