There has been a rise in the number of machines that can learn and perform on their own, all thanks to the huge advancements in the field of Artificial Intelligence (AI). But, when it comes to producing acceptable accuracy rates, these machine-driven systems seem to lag. Machine-based classification combined with human feedback is the best approach for developing accurate and precise Machine Learning models. This is the exact core philosophy behind the concept of Human-in-the-Loop Machine Learning.

What is Human-in-the-Loop Machine Learning?

It is a mix and match approach that utilizes the impactful combination of machine and human intelligence in developing ML models. Human-in-the-Loop (HITL) approach involves integrating feedback from humans into the learning curve of machines that make them more efficient and accurate.

This approach is mostly a variant of the Pareto’s 80/20 rule wherein the algorithm is allowed to learn on its own 80% of the time with the humans’ involvement limited to 19% and remaining 1% left to randomness.

The involvement of humans is restricted to training, testing, and tuning of a model. Data Labeling is the first step that provides best-quality training datasets to machines helping them to learn to make accurate predictions. After training, the humans will fine-tune the model in various ways to avoid any overfitting and make the model learn about classifiers for rare or edge cases in its purview. Later, they further test and validate the model. The above steps form a part of a continuous feedback loop.

When Human-in-the-Loop Machine Learning Matters?

Cost of Error is High

Even a small error can cause dire consequences in certain scenarios. HITL can play a crucial role in developing ML algorithms with no room for error.

Class Imbalances

Machines may not be in a position to predict accurately in case of rare occurrences. In such cases, human involvement helps to resolve any issues and retrain the model to act with high confidence.

Data Availability

There can be instances where ML algorithms may not be able to give optimum performance due to the scarcity of data. For the classification of social media posts during the initial stages of a new business or a start-up, humans can judge better than ML models as they may require time to learn and master the task.

Human-in-the-Loop Machine Learning Applications

Traffic Cameras

The variations in color, text, and size of the traffic signs based on country and area makes it a difficult task for ML algorithms to understand the traffic signs. By providing the labeled datasets, humans can train the ML models to detect traffic signs with no room for error thereby helping to avoid fatal accidents.


Chatbots are trained to analyze and understand the queries of customers and offer a relevant solution. Many a time, customers enter elaborate questions that will confuse the bots which in turn causes them to give out irrelevant answers. Human intervention at this point helps to identify the core issue and resolve it efficiently.

About RightClick.AI

RightClick.AI helps Artificial Intelligence companies develop smart ML models by providing them with best-quality datasets that can be used to train, test, and validate their ML algorithms. If you are looking for pixel-perfect data labeling services, drop a mail to or leave a comment below.

Leave a Reply