Data scientists and computer vision engineers are currently engaged in a race to clean up and structure the monolithic amount of datasets that are being used to train artificial intelligence (AI) and machine learning (ML) algorithms.
This task is at the heart of AI development and requires not only an advanced set of data engineering skills but the ability to think analytically and strategically, too. That’s why data and image labeling tasks are often completed by data labelers, with management and quality control carried out by data scientists.
However, the amount of this data, which is used in applications including self-driving cars, automated document review, and diagnosing medical conditions is growing rapidly, and this is leading to concerns surrounding quality control.
The human touch
Around 80 percent of the time spent on an AI project involves cleaning and labeling data for ML models; it is a very intense process that directly influences the quality of the result. High-quality data labeling leads to better performance while low-quality and incorrect labeling results in a model that will find it more difficult to learn and develop.
The success of data labeling therefore boils down to the work of the highly skilled humans who are responsible for labeling and structuring data for ML.
Analyzing image labeling accuracy
Analyzing data and image labeling accuracy is an important step in the quality control process and consists of both manual and automated steps. Multiple approaches are often combined to cross-check and ensure that a given dataset is faultless. Data scientists tend to agree on a few distinct “hallmarks” of what constitutes a high-quality dataset:
First, the dataset itself matters. The balance and variety of data points within the dataset are an indicator of how well an algorithm might be able to predict similar data points and patterns. Where there are imbalances, techniques such as oversampling or weight balancing are employed.
How precisely and consistently labels are placed on a data point also plays a huge role. It is important to measure both data labeling accuracy and how consistent it is during the quality assurance process.
Methods for ensuring image labeling accuracy
How can expert data labelers continue to ensure quality and accuracy in this way when they are having to work through increasingly bigger piles of it, though?
It all comes down to how data labeling is being managed by an organization. There are three main ways to do this:
- Employ full-time data labelers
- Outsource data labeling to freelancers/contractors
- Crowdsourcing data labeling
While full-time data scientists can manage data labeling tasks with a decent level of quality, it is difficult to scale teams to meet fluctuating demand and dataset sizes. You’ve also got to account for employee turnover, training requirements, and bringing new hires up to speed.
An alternative option is the second, outsourcing to freelancers and contractors, but recruiting and managing freelancers takes time, and freelance workers might not be subject to the same skills assessments as full-time employees.
The third option is sending your data tasks to several data labelers at the same time through a crowdsourcing platform like Tasq.ai. Here, quality is assured via consensus: lots of data labelers complete the same task, and the answer that is provided by the majority of the labelers is chosen as the correct one. The larger the team of labelers, the higher the accuracy.
Ensuring accuracy through confidence levels
Tasq.ai is a robust data annotation platform that delivers data visibility and transparency for ML teams.
To help us make more accurate decisions regarding which image labels are correct, we collect a wide variety of unique judgments from a cohort of vetted data labelers who come from a range of different backgrounds. These people are known as “Tasqers”, and together they make up a global crowd of experts who provide flexible and scalable data labeling to businesses that need an on-demand, pay-as-you-go solution.
During the labeling process, thousands of different expert-vetted Tasqers come together to complete a data labeling task. This allows the Tasq platform to leverage multiple judgments and choose the answer with the highest confidence level.
Continuous quality assurance
While leveraging multiple judgments is often enough to avoid any problems, we acknowledge the importance of continuous quality assurance. That’s why we constantly assess our team of labelers.
As our workers annotate images, we build up multiple judgments for each individual. Using statistics, we’re then able to vet each worker by looking at how often they agree or disagree with other workers. This allows us to flag when a worker might be producing low-quality work (i.e., they disagree with the majority often) or where there might be a labeling issue (i.e., a worker who normally agrees with the majority now disagrees).
This QA approach allows us to i) identify and remove low-quality workers from the platform and ii) quickly identify and rectify labeling mistakes. The result? High labeling accuracy that bolsters your ML model’s predictive capabilities.
See the platform in action
If you would like to find out more about how our powerful data annotation platform can help to take your ML project to the next level and see how it works in practice, get in touch today!
You can also request a free 30-minute demo.