It’s More than AI: The Rise of Human-Assisted Data Labeling

Published on June 4, 2021
Last Updated on August 25, 2022

Artificial intelligence (AI) and Machine Learning (ML) are making huge waves across all industries. However, no matter how cutting-edge and groundbreaking, these technologies are only as good as the data that powers them1. As the quality and variety of data continue to increase exponentially, the success of a model greatly depends on how a machine makes sense of it.

This is precisely why data labeling is crucial. It is defined as the task of annotating data—usually in the form of images, videos, audio, and text—with labels. These labels add meaningful values to a piece of data, providing the necessary context for AI models to learn from it better2.

To put it simply, before a  driverless car can hit the streets, it needs to go through a rigorous process applying context to specific parameters such as shapes, objects, colors, sizes, angles, and distance. An autonomous vehicle collects data through sensors, which will be used for visual detection and interpretation. However, before a computer vision model can navigate on its own based on the real world data directly it must be properly trained using high-quality data. Once that is done, the model can infer or detect new patterns and classify objects precisely, allowing the car to drive itself.

How exactly does data labeling work?

Data labeling is an integral step in the preprocessing stage of building AI models via supervised learning, where learning algorithms are applied to map one input to an output. It works by having a labeled set of data that the model can learn to make correct decisions. An adequately labeled dataset used as the objective standard to train and evaluate a given model is called the training data. 

The accuracy of your training data will determine the accuracy of your trained model thus, putting in great effort and investing in high-quality data3.

But Why are Humans Important in Driving Data Labeling Technology?

Humans play an integral role in the AI model’s supervised learning. The development of the training data is accomplished through data experts as they make judgments on given pieces of unlabeled data4. For example, an expert may be tasked to annotate videos that contain vehicles. Labeling could be as unrefined as a simple “yes” or “no,” or as specific as identifying which individual pixels belong to each object car within the instance segmentation. 

However, the role of humans does not end there.

Human intervention must be maintained during the testing phase to provide performance monitoring. Applying a human-in-the-loop (HITL) configuration means continuously involving people in the cycle of processing, judging, and improving the model. Through the means of human judgment, can identify gaps and the necessary insights generated to recalibrate or retrain as necessary5.

The explosion of artificial intelligence, machine learning, and even more so deep learning, means the rise of spending on data labeling.

Nearly all industries can benefit from leveraging AI and its capabilities. The explosion of ML also brings with it the rise of spending on data labeling services. According to Global Market Insights, the data labeling market is expected to expand at a compound annual growth rate (CAGR) of over 30%, reaching around $7 billion by 20276.

Here are the different examples of Data Labeling services across industries:


  • Medical image labeling for early detection and diagnosis of illnesses7.
  • Analysis of medical records for improving drug prescriptions and repurposing8


  • Development of multi-functional autonomous robots for industrial and commercial applications 
  • Voice and facial recognition for smart devices such as phones, laptops, security systems, and televisions for accessibility and ease of use9


  • Recognition and differentiation of products in a customer’s shopping cart for autonomous billing and checkout
  • Shelf content management and shelf product placement optimization for maximizing sales
  • Analysis of foot traffic trends and optimization of staff presence in-store aisle for better customer assistance10

Travel and Transportation

  • Intelligent voice assistants for in-vehicle navigation assistance or tour guiding and booking automation purposes
  • Development of self-driving vehicles and intelligent traffic systems11 
  • Early detection of possible critical collisions through integrated cameras and dash cams for driver safety12


  • Aerial crop monitoring for analyzing field conditions and plant health
  • Segmentation, health assessment, and behavioral monitoring of livestock for increased productivity13

Scaling Results with Data Experts

The quality of your AI is only as good as the quality of your data. And fine-tuned technologies are vital to achieving this. However, Teammates with the essential skill sets and expertise ensure that accurate data and insights are served—resulting in an overall better performance model.

Data experts are an integral part of client experience. It is why investing in robust training, industry coaching, holistic wellness, and a positive company culture provides fruitful results. When employees enjoy and take pride in what they do, they go the extra mile and produce incredible results.

Combined with advanced solutions and strategies, well-rounded data experts make up the foundations for how AI Operations cater to the most challenging AI applications.

Here are a few examples of how TaskUs delivered expert AI Operations solutions to our clients:

  1. Leading Autonomous Vehicle Company: Provided optimized data annotation and labeling services to achieve rapid scaling of operations
  • Achieved 98% accuracy score versus the client’s 90% target
  • Surpassed the targeted QA score of 90% by 25 points (115% score) through a proactive approach to efficient quality management
  • Exceeded throughput targets by 20%, resulting in accelerated project completion
  • Scaled operations by 1000+ Teammates in less than four months, enabling the client to accelerate the training and performance model rapidly 
  1. Top Social Media Video and Photo Sharing Platform: Trained and refined existing sub-optimal ML models to improve user experience
  • Reduced new Teammate ramp time down to two weeks vs. former partner’s five-week timeframe
  • Improved false-positive results by 50% on ML model performance in less than two months 
  1. Global Cosmetics Retailer: Provided AI-assisted toolsets to automate processing sentiment analysis and tagging
  • Improved survey response accuracy by 48%
  • Increased throughput efficiency by 60% through smart-tool features

Ready to experience top-notch AI Operations to give your business the extra edge? Click here to find out more about our services.

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John Schauf