AI models are trained using massive amounts of data gathered over time. However, the AI’s role is to solve specific problems rather than general ones. The chances of not finding the required number of data sets for your specific problem are extremely high. There is a chance that the team will embark on a data-gathering marathon only to hit a roadblock known as a data trap. To make presumably accurate predictions, the AI models’ understanding is based on sheer numbers and cold calculations. However, they do not have the same level of certainty in understanding the context that humans do.
To compensate for this gap, human involvement is regarded as an unavoidable component in the execution of an ML cycle. This is where the HITL, or Humans in the Loop, mechanism comes into play. A human-in-the-loop model allows humans to validate a machine learning model as correct or incorrect during training.
A machine learning project begins with data preparation, which unfortunately consumes the majority of a project’s valuable time. Data preparation is critical because failing to spend enough time understanding and labeling the data is a sure recipe for project failure. The labeling task in the HITL model is assigned to a well-informed human being who can differentiate and categorize to make the job of a machine learning algorithm in selecting the right set of data easier.
The Pareto principle, which ML developers sincerely follow, boils down to how much a human should be involved – 80 percent computer-driven AI, 19 percent human input, and 1 percent randomness. DeepMind, Google Health’s medical AI system, detected more than 2,600 breast cancer cases in 2020 than a radiologist could. There is always the possibility of an exception in medical cases. The argument here is that using the HITL model would improve diagnostic test accuracy, as only a few cases might turn out to be non-cancerous cysts. We would definitely prefer 99 percent accuracy over 80 percent accuracy.
Why is HITL so important in the development of ML models?
To answer this question, we must first comprehend what occurs throughout the cycle. Humans label the data first, as part of data preparation, so that the models are fed only high-quality data. Given the variety and complexity of practical situations, an ML model should be tuned to all possible scenarios, which may include overfitting, teaching classifiers about edge cases, or adding new data categories to the model’s domain. In many cases, despite all of the training and tuning, the model becomes unconvinced about a judgment or overconfident about an incorrect decision. A human can simply swoop in with his feedback in the HITL model.
Thus, HITL accomplishes what a human or a machine could not accomplish alone, and the machine learns to perform better with continuous feedback. HITL also offers a larger playground for testing ML models, which is an important MLOps practice.
When Big Data caves, HITL has your back
When the data set is too small, the likelihood of overfitting the data values is high. This means that the model generalizes over a small set of data, and when presented with rare values, the conclusions reached are a direct result of the pattern it learns from the unrelated data. This problem can be solved by adding more data, increasing the size of the data set through data transformation techniques, regularising the data, discarding features from the data, or boosting the model complexity.
Similar techniques work in the case of underfitting when the model fails to recognize the underlying pattern simply because it has some outliers distorting the picture. All of these techniques have a few drawbacks – they are undesirable and result in suboptimal predictions. HITL can assist in two ways. The ML engineer can either pause the model to readjust it before restarting with improved architecture or attempt on-the-fly label correction to mitigate classification errors. Because past performance cannot guarantee future results, ML models are doomed to drift with changing databases, necessitating adjustment. HITL is the rudder in all of these cases.
