Human-in-the-Loop in Machine Learning: Why Does It Matter?

Machine learning is becoming an increasingly important concept for modern businesses of all kinds, from fintech to health care and from heavy machinery to retail. And the reason for such popularity is simple: ML allows us to cut corners by making accurate predictions and introducing automation. For instance, expert computer vision services or NLP services help businesses streamline processes and save valuable resources, both in terms of time and HR.

This, however, brought on concerns paired with the wide discussion: if artificial intelligence is used to automate the workflows, does it mean there will be no place left for human workers? And while such claims have a sound ground, there’s also a different point of view that’s worth considering. ML also generates new positions due to the essential concept of human-in-the-loop, also referred to as HITL.

In this article, we’ll be taking a look at what the concept of human-in-the-loop in machine learning means, why it’s important, and what are its examples from the real world. Let’s dive in!

Human-in-the-Loop Meaning and Value for AI and ML

Artificial intelligence and its sub-discipline, machine learning, are both aimed at making the work of people easier. The machines can now help with the most tedious and laborious tasks, as they don’t require sleep or rest and can do repetitive work with more ease and precision.

However, that doesn’t mean that people are out of work now that the machines have been giving the spark of artificial intelligence. On the contrary: human experts are as valuable as ever since it’s up to people to make all the creative decisions. That’s why the human-in-the-loop concept has been gaining tremendous popularity in recent years.

But there’s more to human-in-the-loop than meets the eye. Aside from why it’s beneficial, it’s also worth acknowledging that there may be different types of “humans” in the machine learning loop.

Human-in-the-Loop in ML: What Is It?

Human-in-the-loop can be used in different contexts and refer to different people. Usually, this term is being used in its narrow sense, where “humans” referred to are the specialists that help reduce the number of errors and supervise the learning process of the machines. These experts may be vendors of all kinds, such as data annotators, QAs, data scientists, and ML engineers.

It’s also worth noting that there is a broader sense to the term of human-in-the-loop. It refers to other experts such as product and project managers, data analysts, and domain experts. These professionals are involved in the planning stages of the model development as they investigate the market needs, evaluate the appropriateness of the model, and establish the ownership of the product.

With the human feedback that the human-in-the-loop concept provides, applications that use artificial intelligence can improve faster and more effectively than training by themselves. Whatever meaning you use in your processes, it’s worth remembering that, despite the automation systems available today, it’s still impossible to completely remove a human out of the loop if you need to complete a complex task.

Here’s a simple example to showcase this point. Let’s refer to the narrow meaning of the human-in-the-loop approach: you have a model that you need to train. To address this, obtaining various types of datasets in machine learning necessitates the utilization of labeling experts to tag every piece of information. Usually, within a large labeled dataset.

Surely, there are automation techniques that can mark the separate data pieces (for example, you can use a tool that highlights each different word in a given text). However, you still need a data labeler to assign meaning to these labels (to mark one word as a “name”, the other as a “product”, etc.).

On the other hand, you can choose to use unlabeled data and unsupervised machine learning to avoid the help of annotation experts. However, the list of tasks for this type of machine learning is quite limited. A sophisticated, smart model will need at least some form of annotation from human experts.

Human-out-of-the-Loop

There is yet another term that should be taken into account when talking about human-in-the-loop. The concept of human-out-of-the-loop may seem like the logical opposite: it is focused on taking people out of the loop and letting the machines do all the learning and decision-making. However, it should be regarded in the context of specific examples.

In certain cases (such as autonomous driving), human-out-of-the-loop may be a sound idea. While people are better at adapting to the rapidly changing environment on the roads, they also are the route cause of the majority of the problems: inattentive or distracted driving, slowed reactions, and biological defects are the most common causes of accidents. Taking people out of the loop (in simple words, only letting the machines drive) may solve these problems and decrease the risks of autonomous driving.

Reinforcement machine learning also allows removing humans out of the loop for certain cases. As this type of machine learning is based on the system of reward vs punishment, it allows the machines to train properly without the supervision of humans. And while the tasks using reinforcement learning are still rather limited to certain areas of human activity (e.g., games like chess and Go), there are proposals that could make the human-out-of-the-loop a valuable addition to the future machine learning tasks.

Here’s another idea for using the human-out-of-the-loop concept to keep you on your toes: the areas with increased danger. While autonomous driving only might be risky, weapons development and deployment is definitely a very dangerous activity. Taking a human out of the loop is not only recommended here but also logical as it supports the humanitarian values of our civilization.

Additional Concepts in HITL

A somewhat modified concept for such cases that combines the benefits of both human-in-the-loop (control over the process of training and decision-making) and human-out-of-the-loop (decreasing risks and improving the time for decision-making) is human-on-the-loop. This hybrid approach is aimed at giving tactical decision-making to the machines while people still hold overall control over the system.

Furthermore, in the field of human-in-the-loop (HITL) systems, the concept of human over the loop emphasizes the active involvement of human experts who provide oversight, guidance, and intervention throughout the decision-making process to ensure accurate and ethical outcomes.

Human-in-the-Loop Benefits with Examples: Why Do People Matter as Machine Supervisors?

So if human-out-of-the-loop may both be effective and decrease the risks for people, why do we still need the human-in-the-loop concept? The simple answer is that, for now, human-out-of-the-loop still only can be applied within very narrow areas. For a majority of cases, human-in-the-loop is what works best.

The fact is that human-in-the-loop systems bring a number of significant benefits to the process of algorithm development. Still doubtful? Let’s see why this concept is essential for developing a machine learning model. Seek out human in the loop examples to uncover the true power of HITL.

— Increasing Quality and Accuracy

Whatever you may give priority to when building a model (from costs to speed to ingenuity), high quality is the only thing that you should never sacrifice. That’s why precision and accuracy become the primary goal when training any machine learning algorithm. In situations where safety is paramount, such as aircraft engineering, you cannot go without a human in the loop.

Naturally, automation is a major benefit of introducing machine learning into a workflow. And the combination of ML and human effort seems to give the best results through human in the loop automation: one of the studies proved that such a combined system outperforms both human doctors and ML-only systems in identifying breast cancer based on X-rays. This means that, while the machines are capable of facilitating the work of human experts and people bring in the quality, it’s a good idea to use both to strengthen the overall effect.

Domain expertise is yet another thing you cannot forget about when building a machine learning algorithm. To design a model that could give you a competitive advantage in the market, it’s important to incorporate sophisticated and unique knowledge only available from human domain experts. Such is the case with the Nasdaq discovery tool that aimed to improve the management of trading activities and was built with the help of human analysts.

This human-in-the-loop benefit also considers covering the corner cases, for example, for the models that require rare or insufficient data to properly work. The absence of good data may also serve as a barrier to training ML models without human supervision. Facebook, for example, monitors social activity with the help of human experts as it’s still hard to teach all the nuances and intricacies of communication.

— Lowering the Number of Errors

A great example of human-in-the-loop and its advantages is data quality assurance, or QA. While certain companies such as UiPath champion the idea of “automation first”, where every person gets a robot, it’s important to keep in mind the essential need for quality control. After all, there’s a good reason why large audit companies invest in the human-in-the-loop concept to automate their document workflow. There will obviously be mistakes. Since the training data that the models used is not perfect, there will be errors and blank spots that a machine cannot uncover on its own.

A human in the loop, on the other hand, is capable of spotting the problematic spots before any damage is done and to re-train the ML model. Curiously, a few global manufacturers have started to use deep learning as a QA tool; however, there’s still a need for a human expert to tell a machine what color and curvature a banana should be to stay classified as ripe and/or edible.

Quality and decreasing errors can also be achieved by detecting and avoiding bias in machine learning. As ML models train, it’s sometimes very hard to ensure that the data used (especially if it’s big data) is not biased. Adding a human into the loop to utilize both machine and human intelligence, as well as human ingenuity, can help to see and prevent the spread of bias as soon as it occurs, not in the final testing stages. Remember the case of an autonomous Uber car that hit a woman on the streets of Tempe, Arizona? While many were pointing at people who didn’t know how to use the new technology properly, the responsibility should properly be shifted to the lack of human supervision that could help uncover the biases in training before any fatality occurred.

— Employment Opportunities

Last but not least, human-in-the-loop allows negating the popular discourse point about AI taking away jobs from people. On the contrary, using this concept means introducing new, better, more interesting, and ambitious options for people. Human-in-the-loop embraces the highly technological era we’re living in and allows people to strive during this time. An Indian market is the primary example of this as the data annotators have been bringing significant growth to the national economy during recent years.

Besides, sometimes a human-in-the-loop means that a human expert is placed outside the loop and can serve as an arbitrator who can be addressed in case a machine makes a judgment error. While the Canadian visa officers were in part replaced by robots, there’s always a human officer you can address to re-negotiate a machine-made decision.

Summary: So Do You Need a Human in the Loop?

As the fascination with artificial intelligence and machine learning grows, it’s important to remember there’s still a big place that people hold in the process of creating algorithms. The human-in-the-loop concept is among the most valuable ones today. While it means that you’ll need to recruit people to do some work (which might seem like the opposite to automation of processes), it’s still impossible to get a high-performing, sophisticated, and accurate ML model otherwise.

Yes, machines can perform tedious and boring tasks better than humans since they don’t need rest and are less likely to make mistakes due to repetition. However, machines still rely on humans to tell them what is what and how to perform certain jobs. They need a person to tell them what features a ripe banana has. They need an expert to explain what a text means. They need people to spot the problematic areas and cover the blink spots or rectify biases in training. Whether it is natural language programming or computer vision, a case of classifying the images of cats vs dogs, or building a model for autonomous driving, humans are to stay in the loop in the foreseeable future.

Whether it’s a case of classifying the images of cats vs. dogs, or building a model for autonomous driving, humans are to stay in the loop in the foreseeable future. And they still make a difference in machine learning, as only with a qualified team of expert annotators, like the ones at Label Your Data, can you obtain the highest quality datasets for your model training needs.

Don’t hesitate to contact us and see what we have to offer specifically for your AI project!

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Written by

Iryna Sydorenko Editor-at-Large

Iryna is one of the dedicated members of the Label Your Data content team who has put all her efforts in developing our knowledge base. Iryna is a seasoned technical writer with wide-ranging experience in artificial intelligence, machine learning, and deep learning. She has been studying the basics of data annotation for many years and is now sharing her expertise on our blog. The technical realm is a true passion of hers, so make sure to check out other articles written by our talented Iryna!