Accuracy and quality are two distinct concepts, even though they are frequently used interchangeably.
It is important to note that the accuracy of data labelling measures how effectively labelled features in data are compatible with real-world conditions. Computer vision models (e.g., putting bounding boxes around things in street scenes) and natural language processing (NLP) models are both examples of the same phenomenon: (e.g., classifying text for social sentiment).
Accuracy in data labelling is a measure of total dataset accuracy.
- Is the quality of your labellers consistent?
- In other words, are your datasets labelled consistently and accurately?
This is still relevant if you have 29, 89, or 999 data labellers working simultaneously.
When training your model and when using the labelled data to inform future decisions, using data of poor quality can have negative consequences for your model on two separate occasions. High-performance machine learning models must be trained and validated using accurate, trustworthy data to ensure long-term viability.
- Data Labelling Quality Is Affected by 4 Workforce Traits
Knowledge and context, agility, relationship, and communication are the four workforce attributes determining the quality of data labelling for machine learning projects. We’ve discovered over the past decade of offering managed data labelling teams for a start-up to enterprise firms.
In labelling, what influences the quality of the information?
- Information and its surrounding context
Your team’s data labelling efforts will be hampered without familiarity with the relevant domain and context to produce high-quality, structured datasets for machine learning. As we’ve seen, workers categorize data more accurately when they understand the context in which they’re doing so.
For example, those who label your text data should be aware that some terms might be used in various ways based on the context of the text.
To properly categorize the word “bass,” they must first determine whether the text is about fish or music. They may need to know how words like “tissue” and “Kleenex” can interchangeably.
Labellers should be knowledgeable about the industry they work in and how their work connects to the problem you are trying to solve to provide the best possible data.
There are advantages to having someone with domain knowledge or a basic grasp of the industry your data serves on your labelling team. They can lead the team and instruct new members on rules relating to context, the business or product, and edge cases. When it comes to the language, structure, and style, healthcare-related writing might be much different from legal writing.
There are many iterations in machine learning. Your algorithm’s output will improve with time since data labelling evolves as you test and validate your models and gain experience from the results of those tests.
Changes to your product, the needs of your end-users, or new products should not limit the ability of your data labelling team to adapt. A flexible data labelling team can adapt when it comes to data volume, task complexity, and time.
The more machine learning tasks your labelling team can handle, the better.
The attributes, characteristics, or classifications of the data that will be studied for patterns to help predict the goal or answer the question you want your model to predict can be provided by data labellers as you design algorithms and train your models.
The way you work in machine learning is always changing. Data labellers must adapt their process based on the results of model testing and validation.
You need a flexible process, people who care about your data and the success of your project, and a direct connection to a leader on your data labelling team so you can iterate data features, attributes, and workflow based on what you’re learning in the testing and validation levels of (ML) machine learning.
You’ll need to communicate directly with your labelling staff. Building a trusting relationship with your project team and data labellers is easy when using closed feedback loops. It’s important that labellers can discuss their findings with you to use their insights to improve your strategy.