Should you crowdsource your data labeling and annotation?

In the dynamic realm of AI and machine learning, the pivotal role of data labeling and annotation often intersects with challenges of scale, accuracy, and speed. Delving into the intricacies of preparing raw data for machine learning algorithms, the question arises: should you crowdsource your data labeling? Explore the potential of harnessing collective intelligence to overcome the hurdles in AI development and unlock a new paradigm of efficiency in handling datasets. However, let’s start at the beginning and first clarify:

What is data labeling?

Data labeling and annotation is key to the process of preparing raw data – images, text-based content, audio or video – to be “read” by algorithms that fuel machine learning for use in technologies such as computer vision and natural language processing.

Why does data need to be labeled?

People train machine-learning algorithms by labeling certain pieces of data in order to provide context. To train an algorithm that’s part of the software steering a self-driving car that a red light means stop; for example, someone must label all of the red lights in various images to create a signal for the algorithm to understand. Once trained successfully on very large amounts of image data, the algorithm will be able to independently understand a red light as a stop signal.

Natural language processing algorithms are trained in the same way. Take, for example, a chatbot you interact with on a website. The more sophisticated versions are trained with huge amounts of labeled text data to recognize the context of written content. If you’re training an algorithm to understand unstructured content, you’ll need even more labeled examples.

AI and machine-learning technology suppliers typically offer pre-trained models, but they still have to be refined and enhanced by the client according to the individual use case, and new models have to be built.

Three critical challenges of data labeling for machine learning

If you haven’t grasped the irony of this situation yet, we’ll spell it out: we implement AI and machine-learning to reduce the time people have to spend on labor-intensive, monotonous tasks. Yet the process of developing AI and machine-learning models is very labor intensive and should be done by humans. At this intersection is where we run into challenges:

1. Scale

You need lots and lots of labeled data – we’re talking massive datasets – to train AI and machine-learning models. While some specific labeling tasks might require specialist knowledge, having data specialists handle all data labeling would be a very expensive endeavor. So while it’s essential that people train the machines by labeling data, the sheer amount of data that must be labeled makes it infeasible to handle in house.

2. Accuracy

Machine-learning algorithms are only as good as the data that feed them. People tend to make errors, especially when the task at hand is repetitive and overwhelming (see Challenge One). Bad data is bad for business. Gartner found that the average annual cost of poor data quality to an organization is $15 million.

3. Speed

Organizations implement AI to gain or maintain competitive advantage. That advantage is lost if it takes forever to get your AI and machine-learning models up and running. Not to mention unstructured data is much more time-consuming to train an algorithm to read. With a structured form you could simply validate the data, but with unstructured content you would first have to tag the data so the algorithm can learn to tell the difference between different pieces of information, independent of location on the document or image.

Crowdsourcing Data Labeling

Crowdsourcing – when executed in a particular way – offers a solution for data labeling that overcomes these three challenges. Here’s how:

Scale: The key to mass scale data labeling with ScaleHub is snippeting – the practice of breaking a document and the various bits of information it contains into contextless pieces. Once snippetted, the bits of information can be safely sent to crowd contributors all over the world who simultaneously label (and validate, if needed) the data. The practice of snippeting not only breaks the larger task of labeling data into many smaller tasks that crowd contributors can work on simultaneously, but also ensures the security of your customers’ data because crowd contributors only see a part of the document—never the whole.

Accuracy: Data labeling via ScaleHub is unique in that we can guarantee a 99.x% rate of accuracy. We achieve this through various methods; for example, we automatically send the same piece of data to be labeled to two crowd contributors simultaneously. If their results match, we call it correct. In the case of a mismatch, we’ll send the data in question to a third, more experienced contributor for a tiebreaker.

Speed: People are best at labeling data for AI models. At ScaleHub, we’ve found a way to make the human-in-the-loop work not only incredibly accurate, but also considerably faster. How fast? We have turnaround times as low as one hour for almost any volume of data to be labeled.

Using ScaleHub’s crowdsourcing platform, you can safely tap into trusted networks of crowd contributors for data labeling at massive scale. You get to focus on innovation while we prepare your datasets using the most up-to-date methods.

Wondering if crowdsourcing data labeling tasks can help you speed up the realization of your AI projects? Contact us now to learn more about our solution.

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