Appen project
Human-in-the-Loop Machine Learning

Humans and machines need to work together, and the human-in-the-loop approach to machine learning leverages the strengths of both to produce the best outcomes.

Human-in-the-loop (HITL) is a branch of artificial intelligence that leverages both human and machine intelligence to create machine learning models. In a traditional human-in-the-loop approach, people are involved in a virtuous circle where they train, tune, and test a particular algorithm. Generally, it works like this: First, humans label data. This gives a model high quality (and high quantities of) training data. A machine learning algorithm learns to make decisions from this data. Next, humans tune the model. This can happen in several different ways, but commonly, humans will score data to account for overfitting, to teach a classifier about edge cases, or new categories in the model’s purview. Lastly, people can test and validate a model by scoring its outputs, especially in places where an algorithm is unconfident about a judgment or overly confident about an incorrect decision.