A Manual for Machine Learning’s Quality Assurance of Data Labeling

January 27, 2022 4 min read By Cogito Tech. 1006 views

The performance of a machine learning model is dependent on the quality of data labeling. The consistency and correctness of labelled data in machine learning are used to assess quality. Benchmarks consensus, review, Cronbach’s alpha test are some the industry standard procedures for calculating training data quality.

One of the most important aspects of your work is determining which mix of these quality assurance processes is best for your project.

How do You Determine the Accuracy of Data Labeling

Various jobs necessitate various data quality measures. Many data scientists and researchers tend to agree on a few characteristics of high-quality training datasets that they use in big data initiatives. The dataset itself is important first and foremost. The algorithm’s ability to anticipate future comparable points and patterns is determined by the balance and diversity of data points inside it.

Second, the precision with which labels and categories are placed on each data point typically determines the quality of datasets for model training. But it’s not just about the quality of the data labelling; it’s also about how consistent it is. During the quality assurance process, both data correctness and consistency are assessed, with different phases that can be conducted manually or automatically.

Methods for Measuring Data Quality

The data labelling process is incomplete without quality assurance. The labels on data must represent a ground truth degree of accuracy, be unique, independent, and useful for the machine learning model to perform properly. This is true for all machine learning applications, from developing computer vision models to processing natural language.

The following is a list of the steps involved in data labelling:

Methods for Measuring Data Quality

Data Collection: The raw data that will be used to train the model is obtained. This information is cleaned and processed to create a database that can be put into the model directly.

Data Tagging: To tag the data and link it with relevant context that the computer may utilize as ground truth, many data labelling methodologies are used.

Assurance of Quality: The precision of the tags for a specific data point, as well as the accuracy of the coordinate points for bounding box and keypoint annotations, are commonly used to measure the quality of data annotations. For assessing the average correctness of these annotations, QA procedures such as the Consensus algorithm, Cronbach’s alpha test, benchmarks and reviews are highly useful.

Consensus Algorithm

This is a method of establishing data dependability by having several systems or persons agree on a single data point. Consensus can be reached by assigning a certain number of reviewers to each data point (as is more usual with open-source data) or by using a completely automated process.

Cronbach’s alpha

It is a reliability test, or how closely a group of things is connected. It’s a scale dependability metric. The presence of a “high” alpha value does not mean that the metric is one-dimensional. Additional analyses can be undertaken if, in addition to assessing internal consistency, you want to show that the scale is unidimensional.


Benchmarks, also known as gold sets, are used to assess how closely a group or individual’s annotations match a validated standard developed by knowledge experts or data scientists. Benchmarks are the most cost-effective QA solution since they need the least amount of overlapping effort. Benchmarks might be helpful as you continue to assess the quality of your output throughout the project. They may also be used to screen annotation candidates as test datasets.


Another way to assess data quality is to conduct a review. This strategy is based on a domain expert’s examination of label correctness. The evaluation is often done by visually inspecting a small number of labels, however, some projects go through all of them.

Other Important Methods Used by Cogito


Annotators are required to assess their work in this phase. Annotators are typically under a lot of time and workload strain, which can lead to errors in their job. Annotators should slow down and take a careful look at their work during quality assurance, which begins with the self-check phase.


You may have heard the phrase “bias” concerning data science in general and data annotation in specific. Annotation bias is a condition in which annotators have a propensity of labelling data in their way, which might lead to biased conclusions about the data. By including cross-checking into your annotation process, the whole work is viewed in a new light, allowing annotators to spot flaws and inaccuracies in their colleagues’ work.

Review by the manager or QC experts

A project manager is generally in charge of overseeing the project on a day-to-day basis. The manager will be in charge of receiving data samples from clients, working on the needed metrics, and training annotators. After the cross-checking is completed, the manager can verify the output at random to determine if it meets the clients’ needs.

Also Read: Cogito Announces the Five Major Trends Shaping Enterprise Data Labeling for LLM Development


Finding the correct techniques and platforms to label your training data is the first step in obtaining high-quality training data. Understanding the value of high-quality training data and prioritizing it will help you succeed with your models.

If your team’s productivity is being held back by a lack of high-quality labelled data, Cogito Tech LLC might be able to help with its low-cost data or text labelling as well as data annotation for a variety of industries, including healthcare, eCommerce, automobiles, agriculture, and other industries that use machine learning to build AI-based models. Our key selling point is the high quality of our data, which has earned us gold status in quality assessments.

If you wish to learn more about Cogito’s data annotation services,
please contact our expert.