One of the terms you might encounter when dealing with artificial intelligence (AI) and machine learning (ML) is human-in-the-loop (HITL). It is just like it sounds. HITL is a branch of AI that relies on both human and machine intelligence in the creation of machine learning models.
A human-in-the-loop approach means people are involved in the algorithm cycle of training, tuning, and testing.
Humans first label data, which helps the model achieve high quality and high quantity training data. A machine learning algorithm then learns to make decisions based on the data before humans begin to fine tune the model.
The model can then be tested and validated by humans through scoring its outputs. This process is especially helpful in instances where the algorithm is not confident about a judgment, or on the other hand, where the algorithm is too confident about an incorrect decision.
The HITL process is a continuous feedback loop, meaning each of the training, tuning, and testing tasks are fed back into the algorithm. This process enables the algorithm to become more effective and accurate over time, which is especially useful for creating highly accurate and large quantities of training data for specific use cases. The human insight helps tune and test the model so the organization can achieve the most accurate and actionable decision.
The Importance of HITL Machine Learning
HITL is an extremely important branch of AI as conventional machine learning models require a large number of labeled data points to achieve accurate predictions. When there is a lack of data, machine learning models are not as useful.
Take language learning as an example. If you have a language only spoken by a few thousand people, and you want to achieve insights into that language through machine learning, it might be difficult to find enough examples for the model to learn from. With a HITL approach, you can ensure the accuracy of these datasets.
The healthcare industry is also one of the most important for HITL systems. A 2018 study by Stanford found that a HITL model works better than either AI or humans on their own.
HITL systems improve accuracy while also maintaining human-level standards, which is important for many industries across the globe.
When to Use HITL Systems
There are a few specific times in the AI lifecycle when human-in-the-loop machine learning should be used:
Training: The most common place data scientists use HITL is during the training phases, where humans provide labeled data for model training.
Tuning and Testing: The other main time HITL is used is in the tuning and testing phases. Humans tune models for higher accuracy, which is especially crucial when the model is unconfident.
It’s important to note that the HITL approach is not appropriate for every machine learning project. It is mostly used when there is not a lot of available data.
Human-in-the-loop deep learning is used when humans and machine learning processes interact in certain scenarios, such as: algorithms don’t understand the input; data input is interpreted incorrectly; algorithms don’t know how to perform a specific task; the machine learning model needs to be more accurate; the human component needs to be more efficient and accurate; the cost of errors is too high in ML development; and the desired data is not available.
Types of Data Labeling for HITL
The HITL approach can be used for various types of data labeling depending on what kind of data sets are required. For example, if the machine needs to learn to recognize specific shapes, bounding boxes are used. But if the model needs to classify each part of an image, segmentation is preferred. When it comes to facial recognition datasets, face markings are often used.
Another major application is text analysis, which enables the machine to understand what is said or written by humans. Because people use different words to express the same meanings, AI systems must know the different variations. Taking things even further, sentiment analysis can recognize the tone of a specific word or phrase. These examples prove why it is so important for the human-in-the-loop approach to be used.
Why Your Company Should Implement HITL
If your business is looking to install a HITL system, one of the most common ways to do this is by using automation software. There is a lot of automation software that’s already built around the HITL approach, meaning it already has the process factored in.
Systems like these enable the company to achieve high-level performance right away and to achieve insights. Machine learning systems are already being implemented throughout nearly every industry, meaning developers must ensure that the systems perform well with changing data.
There are many advantages to implementing an HITL system into your company:
Improves Decision-Making Process: An HITL system improves the decision-making process of a company by providing transparency and consistency. It also protects against bias by including human feedback in the training process.
More Efficient: HITL systems are generally regarded as more efficient than traditional machine learning systems. They require less time for training and tuning, meaning they produce insights quicker.
Transparency: Human-in-the-loop systems provide greater transparency into the machine learning model, how it works, and why it arrived at a certain decision. Explainability and accountability are fundamental to today’s AI systems, and the HITL approach helps greatly.
Challenges of HITL Systems
Human-in-the-loop systems also present some specific challenges that should be addressed. For one, humans make mistakes, so any system with humans risks being wrong. This can have a big impact on the effectiveness of the system. For example, if a human makes a mistake when labeling data, that same mistake will make its way through the entire system and can cause future problems.
HITL systems can also be slow since humans are involved in the decision-making process. One of the biggest reasons behind the growth of AI and ML is that machines are incredibly faster than humans, but this speed often seen in traditional ML systems won’t always translate into HITL systems.
One more challenge of HITL systems is that they can be expensive to construct and maintain. Besides the costs associated with the machine, the business must budget for human labor as well.