To learn and make accurate predictions, machine learning models need labeled data. Advancements in artificial intelligence (AI) and large language models (LLMs) are driven more by data quality than quantity, or by model architecture. This means that data labeling of a high quality is more important than ever, and even though there are more automated data labeling tools, human expertise is still invaluable. Due to their reliance on predefined patterns and statistical models, algorithms may overlook or misinterpret context, emotions, and other subtle nuances that humans are good at understanding. For instance, human annotators are able to recognize irony, sarcasm, cultural references, and emotional undertones in tasks like sentiment analysis and image labeling that may be difficult for machines to accurately detect. In addition, algorithmic approaches can benefit from human feedback over time.
Organizations can reduce the risks of biases and errors that automated tools alone might introduce by keeping humans informed. I’ve tried a lot of different ways to build a data labeling team over the four years I’ve been leading AI development projects and scaling teams. In this article, I break down the different types of labeling teams, recommend use cases, and offer specific guidance on how to structure, recruit, and train your team.
Various Sorts of Data Labeling Teams When it comes to data labeling for machine learning, there’s no one-size-fits-all solution. Different projects demand different strategies based on their data types, complexity, and intended use cases. The spectrum of data labeling teams generally spans three main types: human-powered (or manual), fully automated, and hybrid. Each approach brings unique strengths to the table, along with certain limitations.
Manual Annotation Teams
Manual annotation teams are primarily made up of annotators who manually label the data. In order to apply context, culture, and linguistic subtleties that machines frequently struggle to comprehend, manual annotation teams rely solely on human cognitive abilities. Projects that require in-depth comprehension and analysis of nuanced or complex data will benefit from this strategy. Manual annotation has scalability and cost challenges: It’s inherently time-consuming and labor-intensive. Even so, subject matter experts are still essential for projects that require high-quality labels, such as medical diagnostics or intricate legal documents. One of the most famous cases of manual annotation is the original iteration of reCAPTCHA. The system was developed by Guatemalan computer scientist Luis von Ahn to protect websites from bots. However, it also significantly contributed to the creation of labeled datasets. When users interacted with reCAPTCHA challenges, like identifying all images with traffic lights or typing distorted text, they also created input-output pairs that were used for training machine learning models in object recognition. (Behavior analysis has since been implemented by the service to identify bots.)
Teams of Automated
Annotators Automated annotation teams rely on algorithms and machine learning models to annotate data with minimal human intervention. The programmatic labeling models that run in the background are developed, trained, and maintained by experts in machine learning, software engineers, and data science. Automated annotation excels in projects such as optical character recognition, which scans documents or images and quickly converts them into searchable text. It is also highly effective in video frame labeling, automatically annotating thousands of frames to identify objects within video streams.
Even though this method has advantages in terms of speed and scalability, it is rarely used by itself because there is little reason to train a new model from scratch using the same labels if you already have a model that can predict them. Additionally, automated annotation is not ideal for data that necessitates intricate contextual understanding or subjective interpretation. It is prone to biases and misclassifications when trained on incomplete or biased datasets because it relies heavily on clearly defined statistical patterns. The requirement for quality control measures and human oversight is emphasized by this inherent limitation.
Hybrid Annotation Teams
The hybrid semi-supervised approach blends the speed of automated labeling with the precision of human oversight to strike a balance between efficiency and accuracy. Large-scale labeling tasks typically use machine learning models, while quality control, edge cases, and ambiguous data are handled by human labelers in this method. In projects like medical image classification, for instance, doctors verify the accuracy of the results after automated algorithms or models first identify potential abnormalities in MRI scans. A key advantage of hybrid teams is their flexibility. Automated models handle repetitive, high-volume tasks that don’t require nuanced judgment, allowing human experts to focus on more challenging cases. This workflow reduces annotation time while maintaining data quality—but integrating machine and human efforts also requires robust workflows and clear communication. Developing guidelines ensures consistent labeling across the team, and continuous feedback loops help refine automated models based on human insights.
How to Set Up Your Data Labeling Team
The kind of data labeling you choose will determine the kind of experts you need, even though the roles may vary from project to project. To create effective workflows, precise roles and responsibilities must be defined. The most important team members and their potential roles in a data labeling project are as follows: Team lead/Project manager: The team lead sets annotation guidelines, deadlines, and important metrics to make sure everyone is on the same page. For instance, if the project involves annotating videos for a dataset supporting autonomous driving, the lead defines specific parameters like frame rate, object categories, and boundary tolerances. They maintain communication between stakeholders and the annotation team, making sure that client feedback (e.g., requiring more precise pedestrian identification) gets incorporated into updated guidelines. In the case of hybrid teams, they ensure models are regularly updated with manual corrections and that timelines for both teams align.
QA specialist: As the gatekeeper for quality, the QA specialist routinely audits annotations to confirm that they meet the project’s accuracy standards. For example, if an annotator consistently mislabels cancerous tumors in MRI scans in medical image labeling, the job of the QA specialist is to catch the discrepancy, work with the team lead to adjust the guidelines, and provide tailored feedback to the annotator. They might run spot-checks or sampling reviews to verify the consistency of the team’s output, which directly impacts the reliability of data models.
Labelers of data: The primary contributors to the actual task of labeling data are labelers. If the project involves annotating e-commerce images for object detection, for example, they would meticulously outline items like shoes, bags, and clothing. They adhere to guidelines for uniform labeling while seeking clarification on ambiguous cases. For instance, in order to ensure consistent labeling, they consult the team lead or QA specialist whenever a new product category like smartwatches appears.
Domain expert/Consultant: In a hybrid approach to labeling, domain experts collaborate with engineers and annotators to improve models for particular problems. They might offer advice in tricky situations where automated models fail, making sure that the rules of the system incorporate expert knowledge. In an e-commerce image categorization project, for instance, they might outline fashion style distinctions that manual annotators would need to identify.
Data scientist: The data scientist defines the strategies for preprocessing and training datasets to optimize the annotation models. Suppose the automated annotation project involves categorizing sentiment in customer emails. In that case, the data scientist creates pipelines for the data that filter, balance, and clean the dataset in order to accurately detect sentiment. They look at annotated outputs to see if there are biases, gaps, or error patterns. This helps machine learning engineers make better models.
Engineers capable of handling development tasks will be needed for hybrid and automated data labeling projects:
Software developer: The infrastructure that incorporates the annotation models into the overall workflow is built and maintained by software developers. They would, for instance, develop a tool to feed real-time video into the models, capture the annotations, and store them in a structured database in an autonomous driving project where videos are analyzed for lane detection. Developers can also implement APIs that enable annotators to query and validate automated results efficiently.
Engineer in machine learning: The engineer in machine learning creates and teaches the models that are used for automated annotation. A convolutional neural network (CNN) capable of recognizing various facial features would be created by the engineer if the project involved labeling images for facial recognition in security systems. To reduce false positives and negatives, the engineer also refines the model using annotated data. Continuous testing and retraining improve the accuracy of the system, especially when new facial patterns or angles are introduced. Centralized vs. Teams for Labeling Data Decentralized The project scope, complexity of the data, security requirements, and budget all play a role in determining which model is best for your data labeling team.
