Many aspects of modern life have been significantly improved and transformed by the automation capabilities of artificial intelligence. In fact, automation is almost everywhere. It’s in your gadgets, it’s used in your educational system or work, it’s in your home assistant, etc.
While the benefits of automation are duly appreciated, some industries require a nuanced approach to AI and automation tools. One such area is data annotation. Traditionally, it’s a fully manual process involving big teams of annotators and subject-matter specialists. But how does AI fit in here?
In essence, AI-assisted data labeling is a relatively new method of work in this niche. However, you might disagree with this, since annotators always relied on automated or semi-automated tools. But human control was still obligatory to ensure the highest quality of data labeling. Can automation do the same and beat human annotators in terms of quality and other advantages? Let’s have a debate!
The Data Annotation Process: Traditional Approach vs. Automation
It’s clear that manual data labeling relies on humans, while the automated approach requires the development of specialized tooling to perform the same process with no human involvement. The reason for increased interest in automation is the manual process of the annotation itself.
Despite high levels of accuracy and low error rate (not always, though), manual annotation of raw data is time-consuming and needs a lot of resources, including humans, money, and time. So, it can be a long-term and costly procedure for your business or individual project.
Here are the key distinctions between traditional and automated data annotation to note:
Manual data annotation process is the diligent work of human annotators who add pertinent labels to images, videos, text files, or even audio data. However, manual data annotation is resource-intensive. Data annotators must sift through enormous amounts of data to create accurate training data for machine learning.
This might delay a project and create a backlog. But when it comes to accuracy and quality, expert annotators are the best choice. The edge instances that automated systems continue to miss can be effectively identified by manual labeling. In addition, human annotators enable quality control over vast volumes of data.
Automated data annotation refers to any labeling process performed by technology rather than a human. For this type of annotation, heuristic techniques, machine learning models, or a combination of the two may be applied. Data annotation automation has the advantage of being economical, and labeling is a rather efficient process in and of itself.
The majority of easy-to-recognize labels may be handled through automated data annotation. Overall, this strategy can speed up the tagging process considerably. However, automatic data labeling still poses a lot of flaws and risks. That might cost a lot of money when mishandled and put into an ML model.
The Perks of AI-Assisted Data Labeling
Annotating data involves a lot of details that a team of data experts needs to find and perfectly tag in a single piece of data. Say you have 50 000 images to annotate, that’s a lot of work for humans and expenses for the client. Leading such a tedious project can be challenging: you need to ensure that everyone is on the same track and follows the predefined standard of quality data labeling.
There’s also a lot of work to be done after the annotation process itself. Again, by humans. Each scenario is different, though. Yet, the foregoing problems of manual annotation can be addressed in part by adding automation to a machine learning project. Of course, you can’t take away at least the minimum human involvement in the project. But reducing it can cut costs, lower the error rate, eliminate the need for outsourcing, and speed up the entire annotation process.
Among other benefits of adding automation to the data labeling process, the most important are:
- AI can handle better workloads and make your project scalable.
This gives you more time to focus on building and perfecting an ML model. Manual data annotation lacks these features and has a limit on how much data a human can work with per shift.
- Automated data labeling is more adaptable to project changes.
Sometimes, the requirements of the project change, and thus data needs to be relabeled. This means a long and laborious manual annotation all over again. It enables you to change labeling settings and functions, so you can get a fresh training dataset much faster. What’s more, automation requires a minimum amount of time and resources.
- It’s easier to manage AI-assisted data annotation.
Manual data labeling makes it challenging to audit the labeling decisions, since there is no record of the reasoning or conclusions that led to the categorization of those labels. As a result, you might face compliance, safety, and quality control issues. And this is when automation comes in handy. It helps eliminate any potential bias or other unfavorable behavior from the annotation process. It does so by tracking every label back to a single inspectable function.
Summing up, by integrating automation into your process, you can address the bottleneck that has plagued ML experts ever since artificial intelligence was first developed.
How to Automate the Data Annotation Process?
Artificial intelligence still poses some serious challenges regarding accuracy, credibility, and bias in the context of preparing training data for ML models. So, for businesses or individual clients relying on automatic tools to annotate data for machine learning projects, there are two viable strategies to include automation in the process of data annotation:
- Pre-annotation. AI is far from flawless, so it’s best for a team of annotators to be prepared to offer evaluations and modifications of labeling when needed. Data experts follow the automatic pre-annotation to check, revise, and finish the annotations. There will always be exceptions and edge cases because automation cannot label everything at a desired percentage of accuracy.
- Reducing the workload. Based on the use case, task complexity, and other considerations, an auto-labeling model may offer a confidence level. Annotations that have lower confidence values are sent to a human for evaluation or correction, enriching the dataset with correct annotations.
Why Adding Automation to the Annotation Project?
For certain projects in machine learning, automation is the most logical and practical choice. And it makes perfect sense: it’s hard to train a model manually when it requires training on millions of data pieces.
The more specific your project is, the more beneficial automation will be because using only human resources might cause project delays and lead to critical errors. Additionally, using automation annotation simply works, as some labeling initiatives go hand in hand with automation.
The Key Automation Tools for Data Annotation
Auto-labeling tools are often part of many data annotation systems and platforms that employ artificial intelligence to annotate data. Human annotators may then validate or modify those labels, which saves time and resources overall. Although automated annotation is not perfect, it can still serve as a good place to start for data annotators and relieve their workload.
As such, there are many companies in the industry that embrace a semi-automated approach in their work to leverage the advantages of both automation and human expertise. For instance, https://labelyourdata.com/ has a big team of professional data annotators who use automated tools. Humans help to follow the data security standards, while automation helps them run the process and handle large workloads as fast and efficiently as possible.
Automated tools for data labeling use AI to put the right labels on a given dataset. Such tools can be used to augment the work of human annotators and cut down on the time and costs associated with this process. These automatic solutions include features that have the potential to drastically alter the workflow of annotation and model development.
The following are some of the most widely used automatic annotation tools, which are frequently used with manual annotation as well:
- Amazon SageMaker Ground Truth
- Label Studio
Businesses used to leave the data annotation process up to data scientists because of its manual and process-heavy nature. However, the rising adoption of AI has forced them to focus on developing automated solutions instead. Today, automation can be easily integrated into the data labeling process and be of great use to humans.
Eventually, automation annotation has been shown to speed up data processing and aid AI experts in completing their tasks on time with the minimum resources required. Nevertheless, when it comes to precision and accuracy in preparing training data for machine learning, skilled human annotators continue to be at the top of the heap.
So, it’s all about finding the golden mean between the benefits of automation and the importance of human supervision for the most optimal annotation results.