1. Insights
  2. AI Data
  3. Article
  • Share on X
  • Share on Facebook
  • Share via email

Five common data annotation challenges and how to solve them

Posted November 4, 2022
Illustration of a mountain made up of data that's topped with a flag

Data annotation is both a critical, and intensely time-consuming, part of the machine learning model production pipeline. This is especially true as more data than ever is being used to push the boundaries of what artificial intelligence (AI) can do — a fact that is leading the global data labeling market toward a value of $13 billion by 2030, according to a report by ResearchandMarkets.

These ever-expanding troves of data are making model training processes longer and more complex. That’s great for accuracy, since the bigger the sample the more representative it is. But this phenomenon is also resource-intensive, creating a higher demand for data annotators.

Research from Cognilytica’s Data Engineering, Preparation and Labeling for AI report shows that more than 80% of AI project time is spent managing data, from collecting and aggregating that data, to cleaning and labeling it. At the same time, changes in the labor market are making it harder to find data annotators with the skills to label specialized datasets, especially with regard to keeping human bias out of AI and ensuring consistency in data quality.

In short, you are not alone in feeling mounting challenges when it comes to data annotation. These issues are precisely why more and more companies are looking for data annotation outsourcing support.

IDC Info Snapshot - AI data solutions hero image

Quality, quantity, and responsibility: Three key data pillars for successful AI implementations

In this IDC Info Snapshot, learn about the three key pillars for any successful AI implementation and the advantages of partnering with a trusted advisor to create and deliver your AI vision.

Download the guide

Challenges in data annotation

Modern data annotation processes present a number of cumbersome challenges. Here are five that come up most frequently, as well as solutions to help overcome them.

1. Lots of data, small teams

The most immediate challenge organizations face is the sheer amount of data needed to train a modern AI model. Not having the right volume of training data can slow production to a crawl.

Annotation takes patience and expertise, and many organizations simply don’t have the resources capable of handling high-volume labeling.

Solution: An effective way of addressing this challenge is to identify your data annotation needs based on the project requirements and leverage the support of a crowd network to accomplish the task. By crowdsourcing, companies can distribute hundreds of thousands of machine learning micro tasks quickly and cost-effectively. However, managing the crowd can come with its own set of challenges, which is where an experienced AI data solutions provider can help.

2. Producing high-quality annotated data at speed

Compounding the challenge presented by volume requirements, many businesses face inefficiencies when it comes to the speed of production. Relying solely on human annotators to complete complex annotation tasks can slow down your data supply chain and project delivery.

Solution: To increase speed and efficiency, organizations may decide to invest in automation tools, which are a great addition to a semi-supervised or hybrid annotation operation. A cloud-based, on-premise or containerized solution can help streamline the annotation process. That said, the first solution you try may not be the right for your project’s particular needs — so be sure to build in time to reassess accordingly.

3. Keeping human bias out of AI

Bias permeates many scientific fields, and AI is no exception.

While many professionals are likely familiar with the concepts of confirmation bias and sample bias, there are some human bias types that may be novel to your annotators. Anchoring bias, for instance, is a tendency to base opinions or observations on the first similar piece of data that is experienced. An annotator may listen to an audio clip that exemplifies a “happy” voice, but will incorrectly categorize it when performing sentiment analysis on later clips because they do not sound similar enough to the first audio clip. In this way, objectivity is clouded because the first observation becomes a de facto default that all others are compared to.

Solution: In order to mitigate bias, collect large amounts of training data, and recruit from a diverse group of annotators to ensure your data is as universally applicable as possible. A bonus tip is to pick a partner with a proven track record of impact sourcing partnerships to help ensure training data is diverse and inclusive.

4. Achieving consistency in data quality

Chart depicting different challenges in data annotation

The annotation consistency challenge usually reveals itself later in the model training process, but must be addressed from the start. Consistency is necessary to maintain high data quality throughout the annotation process. Research from TELUS International, in collaboration with Pulse, shows that data quality was seen as the biggest challenge when conducting a data annotation project. Poor data quality can lead to skewed results, affecting the overall accuracy of your machine learning model. Lack of consistency can also show up in terms of communication issues and problems in the review process. Solution: Consistent data annotation means annotators share a common opinion or interpretation of a given piece of data. A powerful way to combat inconsistency and other data quality challenges is the re-evaluation of annotation tools and communication processes. Are annotators properly trained to work with your tools of choice? Do the tools themselves fit your needs? How can managers and business leaders better communicate their needs with annotators? Just like machine learning models require iteration and reconsideration over the course of their development, your annotation process do as well.

5. Preventing data breaches

Security should be at the forefront of any tech professional’s mind, and data annotators are no exception. There are some obvious security don’ts, such as crowdsourcing personal or identifying information, but many organizations may not take the extra steps needed to secure their data.

Solution: Non-disclosure agreements, SOC-certification and state-of-the-art deep learning models that auto-anonymize images are important ways to help protect confidential and sensitive data. Working with reputable data annotation companies can also help to ensure strict security measures are in place for staff tasked with handling personal information.

Benefits of outsourcing to an experienced AI data annotation partner

Whether you’re trying to solve for speed, bias or any other type of challenge in the annotation process, working with an experienced and knowledgeable AI data solutions provider is one of the best ways to overcome obstacles. A data solutions partner can take the burden off of your organization and allow your machine learning teams to focus on developing cutting-edge technologies. Speak to one of our AI experts to learn how TELUS International’s data annotation solution can help with your machine learning projects.

Check out our solutions

Enrich your data with our range of human-annotation services at scale.

Learn more