Power Of Human In The Loop Inner

As data becomes the new fuel powering businesses, there is a pressing need to make it more usable for AI and machine learning models. Data tagging enables organizations to organize and enrich their data and make it capable of fueling AI applications and processes. 

By processing millions of data points, data tagging helps in creating powerful, usable databases, so machines can optimally perform the required tasks. But completely relying on AI for data tagging is not advisable. Human expertise and oversight are always needed. Read on to learn all about the power of human-in-the-loop! 

The Need For Combining AI And Human Tagging

Ever since the concept of data tagging came into being, data engineers have been using highly intelligent platforms to identify and annotate specific data. And for a reason. Manually labeling data for AI can get extremely labor-intensive. It can take weeks or even months for humans to label data. The quality and accuracy of manual labeling are also questionable. 

No doubt, modern platforms leverage the many capabilities of machine learning and artificial intelligence for effective labeling, curation, tagging, and annotation of data. But regardless of how efficient they are, the outcomes they generate must be thoroughly checked by quality analysts. Having humans verify and validate the tagging results of AI platforms can go a long way in ensuring the quality and accuracy of the data in question.

This is particularly important in challenging domains like healthcare or autonomous vehicles. Since errors in AI training data can impact the performance of critical AI models, humans can carry out high-quality assessments and help structure and label data more efficiently. In addition, human interception can also ensure services are customized to meet unique client requirements. 

In essence, the best way to scale data labeling is via a combination of AI and human expertise. Combing AI and human tagging can help: 

  • Collate images, text, audio, and video data from different sources, spread across different locations and in different formats.
  • Match people with the right tasks so experts focus only on the work that’s important to them while allowing advanced tools to do all the heavy-duty work. 
  • Better organize and enrich content to be searchable and capable of fueling artificial intelligence and machine learning applications and processes. 
  • Streamline object marking with pre-defined bounding boxes and accurately classify images based on business rules like gender or age group. 
  • Ensure the creation of the best training data for machine learning & artificial intelligence via the use of intelligent image-capturing tools.
  • Machine learning algorithms differentiate between objects in videos and extract relevant information from video content.
  • Eliminate any errors in training data by undertaking a meticulous process of generating, annotating, and validating a high volume of unique datasets to meet the exact requirements of ML applications. 

The Importance Of Balancing The Use Of AI With Human Expertise

When it comes to preparing data for AI and machine learning algorithms, data tagging takes up a lot of time. And while most organizations fall back on advanced tools, including human annotators in the process can help in efficiently labeling a large collection of unstructured data.  

When tagging sensitive data, in particular, companies struggle to set up their own data labeling team. This makes balancing the use of AI with human expertise extremely important. Here’s how you can do it: 

1. Build A Capable Team Of Data Labelers And Engineers

When building a team, make sure to involve data labelers and data engineers. While labelers can help in annotating data and ensuring that data is cleaned and ready to be ingested for model training, data engineers can help in providing a high overview of label consistency and benchmarks to be met by the labelers. 

2. Establish A Baseline Standard

Having data engineers in the data tagging process is not just important for maintaining consistency. Since engineers are familiar with the end-use case of the machine learning application, they can also help keep biases to a minimum. By setting up an appropriate labeling guide and performing consensus, data engineers can help establish a baseline standard and ensure the quality of labeled data is up to the mark. 

3. Choose The Right Tools

The market today is brimming with AI-assisted data tagging and annotation tools. For best results, it is important to choose the right tools. You can either adopt an out-of-the-box tool or build your own labeling software to meet your needs. Regardless of your choice, make sure the tool is easy to use, cost-effective, and designed to boost labeling productivity and efficiency. 

4. Maintain Equilibrium

Although having annotations validated by humans is important from a quality perspective when there is a humongous volume of data, such an approach is impossible. To maintain equilibrium, it is important to have humans review a sample of labeled data and make appropriate conclusions. It is also vital that human feedback is analyzed and incorporated to improve the data tagging and annotation process. 

5. Engage With Experts

Given the volume, velocity, and variety of business data generated today, engaging with experts is a great way to improve the availability, quality, and consistency of data. A proficient team of expert taggers and annotators can deliver high-quality data labeling, curation, tagging, and annotation services and help structure and label data efficiently. They can also make sure to customize these services to meet unique requirements.

If you find yourself being held back by the lack of high-quality labeled data, investing in an AI-assisted data tagging tool can go a long way in increasing your team’s productivity. Although off-the-shelf tools come with trained models, you must also have a qualified team of human labelers and annotators to fuel a new AI revolution. 

Combine AI with human judgment today to accurately tag, label, and annotate your data, make the right decisions, and witness a measurable impact on your business. Kickstart your AI-human tagging journey today with EnFuse. Contact us to learn more!