Blog Post

The importance of data labeling and how we ensure high quality

Artificial intelligence (AI) is only as good as the data that it is trained with; the quality of the data that an AI algorithm is trained with correlates directly with its success. It should therefore come as no surprise that, on average, 80% of the time spent on AI algorithm development involves data preparation and training—data labeling is a huge part of this.

All AI algorithms begin life as a basic model. Developers will start with a huge amount of data, and accurately labeling this data is a critical step for training the algorithm and ensuring that what it learns is accurate.

But what is data labeling, and how is it possible to ensure confidence and accuracy when the labeling process not only involves many datasets but multiple people responsible for labeling these datasets, too?

What is data labeling?

AI and machine learning algorithms learn from labeled data. This makes data labeling one of the most crucial parts of algorithm development.

In short, data labeling—also known as data annotation, tagging, or classification—is the process of preparing datasets for algorithms that learn to recognize repetitive patterns in labeled data.

Once enough labeled data has been processed by the algorithm, it can begin to identify the same patterns in datasets that haven’t been labeled. As you rinse and repeat this process, the algorithms behind AI and machine learning solutions grow smarter and more efficient.

Want to learn more about what data labeling is? Check out our blog post!

Why labeling is important

More and more businesses are adopting AI and machine learning technologies to automate their decision-making and uncover new business opportunities. But it is not as simple as things may seem.

Data labeling allows AI and machine learning algorithms to build an accurate understanding of real-world environments and conditions, and the data labeling market is expected to grow at a compound annual growth rate (CAGR) of 30% by 2027 to a huge US$5.5 billion in value.

To effectively deploy AI models in real-world applications, it is important that application stakeholders know how confident a model is in the predictions it is making. This can be traced all the way back to the data labeling stage, and it is therefore key to ensure that workers involved in the labeling process are being assessed for quality assurance purposes.

With a robust quality assurance process in place, an AI model has a much higher chance of learning and achieving what it is designed to do through a process known as ‘garbage in, garbage out’ — the concept that says the quality of the output is determined by the quality of the input.

How ensures quality

To help us make informed decisions about the correct image labels, collects judgments from multiple people. By doing this, we keep only the best workers (or “Tasqers”, as they’re known internally!) and ensure the highest levels of quality.

During the initial training phase, we compare potential Tasqers’ answers with pre-labeled datasets. This ensures that the potential Tasqer can solve necessary tasks and deliver on expectations. Once a Tasqer is vetted and approved, they are then periodically assessed against pre-labeled data again as a quality control measure. This ensures that the work that they are doing is remaining consistent and on track.

We are aware that pre-labeled data couldn’t possibly cover all labeling scenarios. This means that while the way we continually assess and evaluate Tasqers is robust, it is not possible to provide a 100 percent guarantee that all Tasqers are producing perfect results 100 percent of the time—any platform that claims that they can isn’t being honest!

A robust and proven process

That said, we are confident in our robust and proven processes and are happy to provide assurances to our customers that they won’t get better data labeling anywhere else.

This is all down to the power of our industry-leading platform, the rich bank of high-quality data sets and statistics we have access to, and the decades’ worth of experience within our core team. On

Furthermore, because of how we constantly assess and analyze our team of workers, we’re able to quickly weed out low-quality workers. As workers annotate lots of images, we build up multiple judgments for each one. Using statistics, we’re then able to vet each worker by looking at how often they agree or disagree with the majority of other workers.

If a worker disagrees too often, it could be an indicator that they produce low-quality work. This enables us to identify potential problem workers and manually review and remove them from the platform where appropriate. On the flip side, if a worker who normally agrees with the majority disagrees on occasion, this could be a sign that there’s a potential labeling mistake. When this flags up, we ask more workers to double-check.

As a worker completes more tasks, they get their own quality score. We also use this to assess the quality and combine it with our proprietary adaptive sampling algorithm.

The result of all these quality control measures is a robust system that produces boundless improvements in an ML model’s capability of making accurate predictions that can be relied on by our customers with confidence.

Want to find out more about data labeling?

According to McKinsey, AI has the potential to deliver US$13 trillion in global economic activity by 2030. And due to the rapid growth of the AI market in general, there’s a massive demand for AI data labeling. Indeed, data labeling will be pivotal to achieving this potential.

If you would like to find out more about how the platform can make your business more futureproof and get ahead of your competitors, contact us or request your free 30-minute demo today!

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