Table of Contents
- How to analyze data labeling accuracy?
- What QA methods we apply for data labeling quality validation?
Though existing since the 50s, AI has been gaining mainstream popularity only for the past few years by now. According to a recent report by Statista, the numbers are striking — so far, it has brought 2.3 million jobs and more than 7 billion of revenue, which are predicted to exponentially increase with time. Indeed, AI is used in every field, from the tech sector to banking, automobile production, healthcare and even agriculture, with scientists and entrepreneurs finding more and more ways to apply it. Even though the AI systems used in these fields are different in its functionality and measures, all are similar in their core principle of design. They all use good quality data.
How to analyze data labeling accuracy?
Different tasks require different data quality measures. However, many data scientists and researchers tend to agree on a few dimensions of the high quality datasets, which they consider for big data projects. First and foremost, the dataset itself matters. Balance and variety of data points within it are an indicator of how well will the algorithm be able to predict further similar points and patterns. As an example, let's think of an autonomous vehicle training dataset, which is supposed to train AI in differentiating between moving and motionless vehicles. If it contains 90% of images of moving cars but only 10% of those parked, it is considered imbalanced. Naturally, this could lead to a high chance of error. To solve this issue, techniques such as oversampling, downsampling or weight balancing are introduced.
Secondly, quality in datasets for model training is often defined by how precisely the labels and the categories are placed on each data point. However, it is not only about accuracy of the data labeling but also about how consistently it is accurate. Both data accuracy and consistency are measured during the quality assurance process, separate steps of which may be performed manually or be automated. Often, different approaches are combined to cross-check and ensure the ultimate faultlessness of the given dataset. Below, you can find a few QA methods of measuring data quality:
- Consensus algorithm
- This is a process of achieving data reliability through agreement on a single data point among multiple systems or individuals. Consensus can be either performed by assigning a certain number of reviewers per data point (which is more common for open-source data) or be fully automated.
- Benchmarking and gold standard
- While being quite similar to the approach above, benchmarking is a more complex and reliable approach to QA, since it uses a certain standard. Using automation, labelers get randomly benchmarked by to make sure that the labels and annotations adhere to the predetermined reference, such as a standard image or text. Whereas, the expert is needed to only create a reference and review the overall quality and potential deviations.
- Cronbach's alpha test
- This algorithm is used as a measure of average correlation or consistency of items in a dataset, which, depending on the characteristics of a research (for instance, its homogeneity), may help in quickly accessing an overall reliability of the labels.
Data quality is one of the reasons AI projects thrive, fail or go over budget. Many project leaders tend to underestimate the scope of the time and resources annotating, cleaning and organizing datasets take. Data annotation is an indispensable stage in machine learning and AI development, since even the most advanced and well-designed algorithms are not able to forecast occurrences or perform tasks accurately without reliable training data. In reverse, correct data labelling yields better model performance and allows scientists and engineers to work on development tasks instead.
What QA methods we apply for data labeling quality validation?
Surely, your team is likely to be under tight deadlines and you would not like to misstep by choosing a wrong data labelling service. When working towards a quality annotation approach, it is best to leave data annotation to professional companies specializing in it. Here at Label Your Data, we understand the importance of high quality data which is delivered fast and use our extensive experience to help your projects run smoothly. We assign you a dedicated team of trained and competent data labelers who work for you and understand your specific product and business requirements. Additionally, we offer flexible and customizable software, which bends to accommodate your research and data quality needs, while being certified by industry standard secure solutions.
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