What is Human-In-The-Loop (HITL) for Machine Learning?

In today’s world, artificial intelligence is everywhere. It can be found in self-driving cars, smart digital assistants, and smart devices that can recognize objects and actions. But this technology doesn’t exist in a vacuum — they require the people and processes that help them become smarter. That’s why we’re excited to share our newest technology — human-in-the-loop (HITL) Machine Learning — which is a one-of-its-kind machine learning process that leverages human knowledge to deliver even more value for them.

There are a number of questions that need to be answered to understand the HITL concept. In this article, we are sharing a few important ones like,

• What are some of the most promising avenues of research?
• What are some limitations of current approaches?
• Are there any business benefits to be gained?
• How can we leverage it to improve your product experience?

What is a human-in-the-loop (HITL)?

The concept of human-in-the-loop, or “HITL,” involves having human workers or “operators” review and assess machine learning results instead of leaving those decisions to machines. This approach can help ensure that algorithms are producing decisions that are fair and accurate, and can also help prevent the harm caused by biased (like when an algorithm determines that all carnivorous species are only animals).

Why HITL for Machine Learning is a Must?

A machine learning model might require human-in-the-loop training for several reasons.

• For a machine to learn, the machine learning loop must have three things:
  1. The ability to make a prediction.
  2. A way to measure if the prediction is right or wrong.
  3. The ability to improve its predictions.

• When the model makes its prediction, it can validate the accuracy of its prediction in one of two ways:
  1. Validation data: Confirm with an already tagged dataset.
  2. Human-in-the-loop: Allow people to validate or negate the prediction – How?

How HITL Impacts Machine Learning Modeling

• There is no data that is pre-labeled. One must create it before performing any data-related tasks. Integrating a HITL is where it starts.
• It is important to keep models updated. Human-in-the-loop learning can help keep models up to date with validation datasets from current trends.
• The data is hard to label through automated means. When unlabeled data is hard to label, sometimes the only way to get that data labeled is through a set of human eyes.

How does HITL benefit Machine Learning?

An algorithm cannot understand unstructured data like texts, audio, video, images, and other contents that are not properly labeled. So, humans label the training data for the algorithm to help machine learning models to understand various scenarios and make the right decision.

human-in-the-loop is not an approach that you can apply to every machine-learning project. Even though it helps machines to learn faster, it is always important to understand the importance of human interactions and what could be the scenarios to interact with humans and solve complex machine-learning problems.

• When algorithms are unable to understand the input action.
• When a given input is interpreted incorrectly.
• Algorithms don’t know how to perform assigned tasks.
• To make machine learning models more efficient and accurate.
• To reduce the errors during the development of ML development.
• When you’re looking for rare data which is not available previously.

ADVANTAGES

Today, a wide variety of machine learning algorithms are being used in various applications ranging from developing chatbots to recommendation engines to virtual assistants. human-in-the-loop covers a diverse set of use cases like data governing and content moderation on social media, manufacturing of semi-autonomous vehicles, monitoring cybersecurity and healthcare, auditing financial markets, etc.

Even when AI and machine learning are widely used across various industries and in every field around the globe, it still requires human-in-the-loop at the initial stages of model development to produce expected complete results.

Here are a few benefits of having a HITL.

• For annotating different types of data labeling. For instance, machines can’t differentiate between text, image, voice, etc. whereas humans can be handy to help the model in annotating and recognizing the input.
• There are various types of algorithms in machine learning and each requires different types of data sets to achieve defined objectives. For instance, if you’re training a model to identify objects, then you need human interventions to identify and appreciate perception-based ML models.
• Machine Learning models can easily become biased because they are trained on data that is raw and biased. Having a human-in-the-loop can identify bias in the early stages of model development to reduce defects and improve accuracy.
• Humans in the loop can ensure the same level of accuracy even for rarer types of data as most popular machine learning algorithms require large amounts of labeled data to produce accurate results.
• Incorporating subject-matter experts helps in creating sophisticated applications for many industries to apply machine learning-driven technologies. For instance use of models for fraud detection.

LIMITATIONS

Every process has its own advantages and disadvantages, the same applies to HITL for machine learning. Eventhough there are numerous advantages of HITL for machine learning, human-in-the-loop itself is a costly and time-consuming process. As mainly data labeling and model quality improvement require specialized and skilled workers, organizations might need to spend more than expected. Moreover, this process is labor-intensive, despite different levels of expenses spent.

Why Human-In-The-Loop is the Future of Machine Learning?

Machine learning is a field that seeks to enable machines to learn without being explicitly programmed. Human-in-the-loop helps to solve the important challenges of programming like bias, human error, and inconsistency. It improves the accuracy and reliability of machine learning systems. It allows machines to make decisions based on the preferences of human users. This helps to build a better user experience and reduces the amount of training data needed.

As the next wave of automation, HITL can upgrade machine learning models to processes and analyze where even there are limits to how smart AI can be. Most models today do not contain all the information required and we need humans in the ML chain to make decisions or pass on intermediate information to guide the system in making decisions. The human-in-the-loop approach benefits all kinds of occupations in acquiring the required knowledge which is previously limited or only accessible to skilled workers. Also creates unprecedented training opportunities to create highly realistic simulation-based tailored training. This helps to manage large-scale data inputs and respond to real-time model interactions to develop or test specific skills.

Finally, HITL for creating machine-learning-assisted humans and human-assisted machine-learning working environments. Human-in-the-loop machine learning can have two distinct goals: making a machine learning application more accurate with human input and improving a human task with the aid of machine learning. The two goals are sometimes combined to offer better results. Machine translation is a good example. Human translation can be made faster using machine translation, for example using google auto search complete we can search for text we intend to translate. More recent applications include voice recognition and search on in-home devices, smartphones, autonomous vehicles, etc.

Human-in-the-loop for machine learning is proving its prominence day by day. With the rise of its applications that benefit model building, training, and decision-making, HITL has seen a large increase in popularity of adopting. As HITL usage increases the speed of the ML life cycle there is a chance for more experimentation and better development. When data is hard to come by, Humans can be handy in collecting or creating, or labeling. As we speak the applications, benefits, use cases, and usage of HITL are limitless.

To know more about how you can take advantage of HITL in your machine-learning environment, we can provide you with a free consultation. Reach us here.