A Guide to Data Labeling: Main Things to Know

Artificial Intelligence is now widespread in all areas of human life, from banking and security to retail or sports. The success of any business often depends on the information on which AI algorithms are trained. Therefore, it is essential for companies to properly prepare their information and one of the ways to achieve accurate results is data labeling. 

Data labeling ensures that an AI model can deliver highly accurate results in correspondence with the defined tasks. However, having the right information is only half the battle because there are many other processes to take care of. So, what is data labeling and how can a company set up an efficient labeling process? Read further! 

What is data labeling and why is it important?

Data labeling or data annotation is the process of adding labels or tags to the information to train a machine-learning model to recognize objects. After training on labeled information, an ML model should then be able to recognize familiar objects in a set of unlabeled data. In this way, a machine learning model becomes “smarter” and will be able to deliver accurate predictions.

For example, a machine learning model learns to identify phones in images. The machine can be given several images of different phones labeled “phone”, from which it will learn to understand the common features of each phone. It can then correctly identify what the phones are among different unlabeled images. 

We’ve answered: what is data labeling? After that, let’s turn our attention to why the process is so important for businesses. Since data labeling allows machine learning algorithms to become smarter, companies that use tagged information can be confident that their ML models produce accurate predictions. This, in turn, means that all data-driven decisions will be highly accurate. Now, the next question is: how do companies acquire labeled data?

Data labeling: what is the process?

The labeling process works by the following pattern:

• Data collection: the process of collecting raw information is then followed by its processing which means all duplicate or incorrect information is deleted or corrected. This is needed, so the information can later be “fed” to an ML model and the model will be able to understand it.
• Labeling: implies going through the information and manually adding tags or labels. Labels will later be used by the machine as a base for answering future requests and recognizing labeled objects.
• Quality assurance: to create high-performance ML models, labeled information must be informative and accurate, so a robust quality assurance process is needed to verify the accuracy of the labeled information. Otherwise, there is a high risk that the ML model will not perform properly;
• Model training and testing: after the quality is tested, labeled data is fed to the ML model for training. A model is usually considered successful if it makes 960 correct predictions out of 1000 examples.

The labeling process involves many automated processes, but it also requires human input – a so-called “human-in-the-loop” (HITL). HITL uses human inference to create, train, tune, and test ML models. In this way, people guide the labeling process and provide project-specific data sets to the models, while leaving the rest to the machine.

Also keep in mind that there are three main types of labeling: 

• NLP (natural language processing): first you need to manually identify important sections of the text to generate a training set and then train the model to recognize certain phrases or sentiments in the speech.
• Computer Vision: is used for automatic image categorization and recognition.
• Audio processing: the process of converting various sounds (not speech, though) into a structured audio format.

Unlabeled data vs. labeled data

We’ve talked a lot about labeled data – but what about unlabeled data that is also used by companies for ML model training? Let’s have a closer look at it.

Unlabeled data contains no labels or names. It cannot be used for prediction and forecasting but gives a general impression of the information. For example, a list of unlabeled emails is labeled either as “spam” or “not spam. Labeled information, on the contrary, is labeled by name, type, or number. In this way, labeled data provides a much wider range of possibilities in terms of forecasting and analysis.

Since these two types of information differ by either having or not having a label, there will be different types of machine learning processes.

Supervised machine learning

Supervised learning is an approach to machine learning where a human labels the information and sends it to a machine for training. Based on the labeled data, the machine can then process unlabeled data and recognize previously learned objects. Examples of supervised learning use cases are personalized recommendations or predictions of stock market risks. 

Unsupervised machine learning

Unsupervised learning is an approach that implies using unlabeled data sets and letting the machine independently categorize objects without any previous knowledge of them. Note that a human does not interfere with the learning process, and this is why this approach is called unsupervised learning. Examples of unsupervised machine learning are exploratory analysis and consumer segmentation.

Now let’s go back to the topic. The choice between the types of information depends on the company’s goals, so let’s summarize the main differences between labeled and unlabeled information below.

Unlabeled data:

• Used in unsupervised machine learning 
• Obtained through observation and collection 
• Relatively easy to obtain and store
• Often used for pre-processing datasets

Labeled data:

• Used in supervised machine learning 
• Requires a human/expert to annotate
• Expensive, difficult, and time-consuming to obtain and store
• Used for complex prediction tasks

The main challenges of data labeling

Despite its advantages, labeling also brings certain challenges that companies may face during the process. The most common challenges are:

• Time and cost: manual addition of labels is rather time-consuming and expensive because a company needs to train employees and hire labeling specialists in a particular field;
• Maintaining tools at scale: to maintain high-quality information, skilled workers and smart tools are required, like AI-enabled annotation, automation, information management, and data pipelines. AI is expected to understand more human tasks, therefore, tools requirements keep increasing;
• Inconsistency: since there are often many people with different expertise and opinions involved in the process, labeling ideas may differ, leading to inconsistency;
• Human errors: labeled information is subject to human errors (coding errors, manual input), which can reduce information quality. Poor quality, in turn, leads to inaccurate information processing and a lack of expected results;

Knowing what problems a company may encounter in the labeling process is essential to avoid possible negative business impacts. And quality is the biggest challenge every company faces. 

Methods of data labeling

To develop an effective ML model, data labeling is essential. Thus, companies must consider the most effective methods to structure and label their information effectively. Among these solutions are:

• In-house labeling: in-house data engineers and scientists simplify information tracking, reduce errors, and increase quality. This approach requires more time and cost and is crucial for various industries, like insurance or healthcare since it often requires consultations with experts.
• Crowdsourcing: it is faster and more cost-effective due to its micro-tasking capability and online delivery. But the quality of work, quality assurance, and project management differ among crowdsourcing platforms.
• Outsourcing: a great option for high-level and short-term projects, but it can be time-consuming to develop and manage. This labeling method implies work with experienced staff and pre-built labeling tools.
• Synthetic labeling: produces new project data from pre-existing information, thus improving quality and efficiency. It requires extensive computing power, which can increase its cost though.
• Programmatic labeling: a process that automates labeling and eliminates the need for human annotations.

Data labeling is not a one-size-fits-all process. It is up to businesses to choose the most suitable method for their needs. In this regard, below are a few criteria to consider when deciding on the most fitting approach.

Things to consider when setting up a data labeling process

Data labeling may look simple, but it is not always easy to perform properly. As a result, companies should consider the following when choosing a suitable labeling method: 

• The size of the company: depending on available resources, a company may either design its own solution or select the one available in the market.
• Employee training: companies need data scientists who can react quickly and change the workflow during the model testing and validation phase. Therefore, employee training is an important aspect to focus on and consider when planning the information labeling process.
• The purpose and goals: companies need to establish project goals, timelines, quality metrics, and other key requirements for successful labeling and analysis.

When you’ve determined the size, employee training, and duration of your project, it is important to be aware of some best practices of data labeling to use it ahead.

Best practices for data labeling 

Businesses can implement these best practices to achieve high-quality results:

• Measure model’s performance: data labels can reflect how your model performs. However, you should evaluate your model’s precision, recall, and other metrics using specialized tools such as Scale validate;
• Collect diverse information: the more diverse the information is, the lower the likelihood of dataset bias is. Thus, by identifying bias, the model can learn better and make more accurate predictions;
• Collect specific information: provide the model with the exact information it needs to operate in a particular field. For accurate predictions, your collected information must be as specific as possible;
• Establish a QA process: use a QA method to assess the quality of your labels and ensure successful project outcomes;
• Establish an annotation guideline: creating informative, clear, and concise annotation guidelines will help you avoid errors during labeling before they affect training;
• Run a pilot project: before starting your labeling process, conduct a pilot test to determine the completion time, evaluate the labelers and quality assurance teams, and improve your guidelines and workflows.

Lastly, we have a controversial issue to discuss.

Can we do without labeled data?

AI training using labeled information is the most efficient and high-quality way to analyze information. Because of the human involvement with labeled information and massive spending of resources, the process of collecting and storing labeled data might be too expensive for some companies. Nevertheless, what can companies do with unlabeled information? 

Recall that unlabeled data is a dataset that has only attributes but no target for prediction. For example, if a dataset is unlabeled, there are a bunch of pictures of dogs and cats, and we don’t know what type of animal each one represents. The ML model can still tell us if the dog pictures are similar to each other and are different from cats. 

Two common use cases for dealing with unlabeled data in unsupervised learning are:

• Clustering: the process of breaking down and grouping information into clusters based on similarity (an example with cats and dogs);
• Dimensionality reduction: the process of simplifying the information by combining certain similarities without loss, losing as little information as possible.

Thus, companies can use unlabeled data to extract certain information and group similar objects together. This can later serve as a base for supervised learning. In addition, companies can combine unsupervised and supervised learning elements into a semi-supervised learning model. The semi-supervised learning approach will train the AI and optimize labeling with a small set of unlabeled information, saving the company resources and time. 

Conclusion 

According to CAGR, the global data collection and labeling market was valued at $1.67 billion in 2021 and is expected to grow 25.1% through 2030. Hence, any business that’s willing to explore the value behind its information, should make itself familiar with the data labeling process and methods for its deployment.