Training data quality is an evaluation of a data set’s fitness to serve its purpose in a given ML use case. Your requirements will be driven by the use case, and you will need to evaluate the quality of your data annotation over multiple dimensions, including completeness, exactness, and accuracy.

The process of annotating data always includes some human decisions. The first challenge is actually to have humans agree on what is a correct annotation of the recorded data, and creating such annotation guidelines is sometimes not as easy as one might think. We are experienced in how to efficiently design annotation guidelines that enhance the quality, and we will share some of our insights in a later blog post.

Why is data quality important?

For example, if you train a computer vision system for autonomous vehicles with images of mislabelled road lane lines, the results could be disastrous. In order to develop accurate algorithms, you will need high-quality training data labeled by skilled annotators. To conclude, high-quality training data is necessary for a successful AI initiative. Before you begin to launch your AI initiative, pay attention to your data quality and develop data quality assurance practices to realize the best return on your investment.

Defining Quality of Training Data

Data quality is an assessment of whether the given data is fit for purpose. Not every kind of data, and not every data source, is useful or of sufficiently high quality for the machine learning algorithms that power artificial intelligence development – no matter the ultimate purpose of that AI application.

To be more specific, the quality of data is determined by accuracy, consistency, completeness, timeliness, and integrity.

Accuracy: It measures how reliable a dataset is by comparing it against a known, trustworthy reference data set.

Consistency: Data is consistent when the same data located in different storage areas can be considered equivalent.

Completeness: the data should not have missing values or miss data records.

Timeliness: the data should be up to date.

Integrity: High-integrity data conforms to the syntax (format, type, range) of its definition provided by e.g. a data model

Standard Quality Assurance Methods

Here are some of the more common data quality measurement processes:

1. Benchmarks or gold set Method

It helps measure how well a set of annotations from a group or individual matches the vetted benchmark established by knowledge experts or data scientists. Benchmarks tend to be the most affordable QA option since it involves the least amount of overlapping work. Benchmarks can provide a useful reference point as you continue to measure your output’s quality during the project. They can also be used as test datasets to screen annotation candidates.

2. Consensus Method

Consensus measures the percentage of agreement between multiple human or machine annotators. To calculate a consensus score, it is necessary to divide the sum of agreeing labels by the total number of labels per asset. The goal is to arrive at a consensus decision for each item. An auditor typically arbitrates any disagreement amongst the overlapped judgments. Consensus can be either performed by assigning a certain number of reviewers per data point or be automated.

3. Cronbach’s alpha test

This test is an algorithm used to measure the average correlation or consistency of items in a dataset. Depending on the characteristics of research (for instance, its homogeneity), it may help quickly assess the labels’ overall reliability.

4. Review or Auditing

Auditing is another method to measure data quality. This method is based on the review of label accuracy by a domain expert. The review is usually conducted by visually checking a limited number of labels, but some projects review all labels. TagX enables companies to easily review quality through a sampling portal: a dedicated portal providing full transparency and accountability on data quality. Your team can get full transparency on the batch’s quality and provide direct feedback to data trainers.

Due to the iterative machine learning model testing and validation stages, we must keep in mind that data quality can change during a project. As you train your model or after making your solution live, you’ll probably find patterns in your inaccuracies or identify edge cases that will force you to adapt your dataset. The auditing method of checking the quality of training data measures the accuracy by having reviewed the labels by experts either by checking on the spot or by reviewing all. This method is crucial for projects where auditors review and retread the content until and unless it reaches the highest level of accuracy.

Conclusion

Creating training data is often one of the most expensive components of building a machine learning application. Properly monitoring training data quality increases the chance of having a performant model the first time around. And, getting labels right the first time (first-pass quality) is far cheaper than the cost of discovering and redoing work to fix the problem. With world-class tooling at your fingertips, you can ensure your labeling maintains the level of quality you need to get the modeling results you want.

With Quality Assurance processes data scientists can:

1. Monitor overall consistency and accuracy of training data

2. Quickly troubleshoot quality errors

3. Improve labeler instructions, on-boarding, and training

4. Better understand the specifics to their project on what and how to label

We at TagX, assure to maintain Quality Standards according to the requirement of the project. We have experts in the field who understand data and its allied concerns like no other. We could be your ideal partners as we bring to the table competencies like commitment, confidentiality, flexibility, and ownership to each project or collaboration. So, regardless of the type of data you intend to get annotations for, you could find that veteran team in us to meet your demands and goals. Get your AI models optimized for learning with us.

Prashi Ostwal - Author