Human-in-the-Loop Machine Learning Approach


Huge advances in the field of Artificial Intelligence (AI) has led to the rise of machines that can learn and perform on their own. But these machine-driven systems tend to fall short when it comes to achieving acceptable accuracy rates. The combination of machines-based classification enhanced by human feedback is the best approach to develop accurate Machine Learning models which is the core philosophy behind the Human-in-the-Loop Machine Learning concept.

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

Human-in-the-Loop (HITL) is a mix and match approach that leverages the powerful combination of human and machine intelligence to develop ML models. This approach involves incorporating human feedback into the learning circle of the machines to make them more accurate and efficient.

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

Humans’ involvement is limited to training, tuning, and testing of a particular algorithm. First, they label the data which provides high-quality training datasets to the machines to learn from for making accurate predictions. Then the humans fine-tune the model in several ways to avoid overfitting and teach a classifier about rare or edge cases in the ML model’s purview. Lastly, humans test and validate the model.  These steps are a part of a continuous feedback loop.

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

  1. The cost of errors is high – In certain scenarios, even a small margin of error can lead to dire consequences. HITL plays a significant role in developing ML models with absolutely no room for error.
  2. Class Imbalances – In the case of rare occurrences, machines may not be able to predict or answer accurately. Human involvement helps to resolve such matters and also retrains the models to perform with a high confidence level.
  3. Less availability of data – When there is a scarcity of data for instance in the classification of social media posts during the early stages of a start-up or a new business, humans can make better judgments than ML algorithms which may require some more time to learn and master the task.

Practical Applications of Human-in-the-Loop Machine Learning

Traffic Cameras

Understanding traffic signs is a hard task for algorithms as there are variations in color, size, and text-based on country & area. Humans can help the algorithms by providing labeled datasets which trains them to identify traffic signs without any errors thereby avoiding any fatal accidents.


Chatbots are trained to analyze what the customer wants and offer the best possible solution. But at times customers may enter elaborate queries that might confuse the chatbot causing them to offer a completely irrelevant solution. Human intervention at this stage to point out the core issue would help to resolve the same.

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Human-in-the-Loop Machine Learning Approach

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