Ground truth data is crucial for companies that develop Artificial Intelligence (AI) and Machine Learning (ML) products that ultimately have human interaction. AI and ML models require massive quantities of data to train.
Collecting that data requires substantial effort. But even when the required data is collected, there’s another crucial step to cover before it can be used: it needs to be annotated and tagged. In this article, we cover the basics of data annotation and tagging for AI and ML, describe some of the major challenges, and provide some insight into best practices.
What is ground truth annotation and tagging?
Annotating and tagging data is the process of adding metadata to collected datasets that AI and ML algorithms use to learn. It usually amounts to adding labels, which can be anything from including a bounding box around an object in an image file, to adding a point marker on a video file, to tagging an audio file as being a male’s voice.
For example, imagine a company was training AI to recognize hands. Data scientists would feed the AI thousands of different images of hands. The AI would take all these images and construct a model of what a hand is and learn to recognize it. But before the images of hands could be used by the AI, an analyst would have to review each image and tag which part of the image showed a hand, and further identify the various elements of the hand to improve the accuracy of the AI’s model. That process of identifying the hand and its elements for the AI is annotating and tagging.
Why is the annotation and tagging of ground truth data important?
Annotation and tagging are at the core of how AI and ML algorithms process data and learn from them. Every dot, every marker, and every bounding box is considered by the algorithm and used for learning. But the algorithm needs to be told what those dots, markers, and bounding boxes mean. The data by itself is of limited utility—to be useful, it must be labeled. The more accurately-labeled the data sets are, the better the algorithms will work.
The challenges that are associated with annotation and tagging
Annotating and tagging data is critical, but it is a long and complicated process. Here are some of the challenges that tagging teams face:
- Limited resources
Annotation and tagging data usually requires an entire team of annotators. It can be resource-intensive and take substantial time. Accommodating the complexity and scale of the task within time and budget restrictions can be very difficult.
- Balancing quality and quantity
AI and ML algorithms require immense amounts of data. Without sufficient data, they can’t work. On the other hand, the data needs to be accurate. Finding the right balance between accuracy and volume is critical.
- Dealing with subjectivity
Some tagging and annotating tasks require subjective decisions by analysts, and they occasionally disagree. In these scenarios, it can be hard to create a system where data is tagged consistently.
The Qualitest approach to annotation and tagging
Our approach is unique because we are one of only a handful of companies that provide an end-to-end solution for ground truth data collection, annotation, and tagging. The key to our approach is designing the project with our clients from the beginning and then collecting the data ourselves. That way, we’re already experts on the project requirements of our clients and even on the data itself. This enables our trained team of annotation analysts to create quality standards for the data and focus on efficiently processing it.
Annotation and tagging is the lifeblood of AI and machine learning
Data can be extremely valuable. It is valuable because it can be used to drive innovation, enable personalized marketing, and power the development of new products. But raw data by itself is not very useful. It must be processed and cleaned before its value can be fully unlocked. Annotation and tagging are essential components of data processing, and it is critical to do them properly.
