For any supervised machine learning model to make correct predictions, the quality of labeled data plays a huge role [link this to “What is Labeled Data” blog]. But to ensure accurate labeling, should you hire human annotators or trust automation tools to perform this task?
The answer to this question is not simple, especially if you don’t know the difference between manual and automated data labeling.
Keeping all this in mind, I will cover these topics in this guide:
- What is manual data labeling?
- How is manual labeling different from automated labeling?
- What are the benefits of manual data labeling?
- When to choose manual data labeling?
By the end of this blog, you will be able to understand the role of human annotators in applications such as self-driving cars and security systems.
What is Manual Data Labeling?
Manual data labeling (sometimes known as manual annotation) involves human annotators assigning labels to data, providing it with information and context. The role of human annotators is to add metadata and labels to the raw dataset so that it can be used for training machine learning models.
Let me put it this way: machines cannot visualize and interpret input in the same way humans do. For most ML models, it is fundamental to first assign labels to raw data (in the form of text, audio, video, or patient medical data). And this process of human annotators assigning labels to each data point is known as manual data labeling.
Key Benefits of Manual Data Labeling
To make life easier for you, I have listed the benefits you get with the manual data labeling approach:
1. Ensures More Accurate Results
Accuracy matters most for any machine learning model. And this is where manual labeling is considered slightly better than automated labeling, especially for complex datasets.
Suppose you have a text dataset containing normal and sarcastic statements. And you want to label this dataset. In such an example, automation tools might struggle. [link to “What is Automated Data Labeling”]
This is because it is difficult to train tools for labeling such datasets, where assigning labels to each element of a dataset is not that simplistic.
But if you put your trust in human annotators, they can generate a labeled dataset, differentiating serious statements from sarcastic ones, much more easily. This is because humans can understand the context of each sentence much better than machines. So, in such cases, choosing the manual data labeling approach is a good idea.
2. Gives You More Control Over Your Data
No organization wants its sensitive data to be accessed via a third-party tool.
And this is where manual data labeling clearly wins; when you rely on human annotators for data labeling, you can easily define rules and guidelines to ensure how the sensitive data is used and handled. [link to “What is annotation guidelines documentation”].
More control over the dataset means more control over the entire data labeling process. In other words, you decide how you can get access to your raw dataset, and how they can use it.
3. Allows You To Handle Complex Data Better
Many times, ML models involve datasets that are far from simple; for example, text samples with multiple meanings, sensitive medical images, and complex legal documents are just a few examples. There are many other examples as well.
If your ML models involve such datasets, ask yourself these questions:
- Are human annotators better capable of handling such datasets?
- Should I trust automation tools to tag complicated datasets?
The answer is that humans can handle complex datasets more effectively, leading to more accurate ML models. While automated tools are great for simple datasets and repetitive tasks, the value of human labelers adding context and metadata cannot be ignored.
4. Greater Customization and Flexibility
It’s a fact that automated data labeling can process large datasets much faster than manual labeling (in most cases). However, if your data or project requirements change frequently, you may need to update labeling rules and guidelines to ensure the automated system handles new scenarios correctly.
In such cases, relying only on automated data labeling is not a good idea. Human labelers can provide greater flexibility when dealing with unfamiliar or constantly changing data. This helps in building ML models that can adapt their decisions as per different real-world scenarios.
5 Disadvantages of Manual Data Labeling
Before moving ahead, keep in mind that a team of human annotators manually tagging data points has certain cons.
- Manual data labeling can be time-consuming, especially when dealing with large datasets containing thousands of images or hundreds of hours of video that need to be reviewed.
- When multiple annotators are involved, they might interpret the same guidelines in a different way. This leads to inconsistent labeling. [link to “What is inter-annotator agreement”]
- The risk of human errors during the labeling process can never be 100% mitigated.
- Hiring and managing an in-house team of annotators to handle all labeling projects can prove very costly to you.
- Scaling becomes a headache for you; the annotation team must be scaled up or down based on the manual labeling task.
Manual vs Automated Data Labeling: What’s the Difference?
Now, I will help you settle the manual vs automatic data annotation debate:
| Factor | Manual Data Labeling | Automated Data Labeling |
| Key Point | Humans assign labels and context to a raw dataset | Labeling a dataset via automation tools |
| Manual Effort | Very high | Low |
| Context added to the dataset | Yes | Limited |
| Speed | Slow | Generally very fast |
| Cost | High | Relatively low |
| Accuracy | High | Depends on the tool used |
| Scalability | Offers limited scalability | Highly scalable |
| Data Security | In-house annotation means higher control over data | More risk of sensitive data leaks |
| Quality Control | Quality checks performed by humans | Also requires validation by humans |
| Best Use Case | Ideal for complex tasks | Suitable for large-scale & repetitive datasets |
What About Hybrid Data Labeling?
There’s another type of data labeling different than both of the above approaches. Hybrid data annotation combines both these processes: automated tools assign tags and human labelers validate each label.
In simpler words, this approach also relies on the annotation team, but only for quality assurance and supervision. This is an indication of why the role of human effort is still relevant today.
Note: Hybrid data labeling is also called AI-Assisted Annotation.
Is Manual Data Labeling the Right Choice For You?
Let me put it this way: both manual and automated data labeling have their importance, and both can deliver accurate results.
However, manual data labeling is the ideal option if your ML models involve:
- Unstructured datasets
- Sensitive data
- Frequently changing data structures
Also, if you’re worried about sensitive data being exposed to third-party tools, relying on human annotators for manual data labeling is the safe choice.
Final Thoughts
I hope this blog has provided you with a clear picture of what manual data labeling is and why human annotators are still needed today.
If you are still searching for the best data labeling team, outsource your projects to Annotation. We offer both manual and automated data annotation services at the most affordable price, starting from just $5 per annotation hour!
With the trust of 100+ customers and years of experience in this field, we deliver accurate and consistently labeled data.
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