Definition
In the field of Artificial Intelligence, Tagging which is also referred to as Data labeling is the process of identifying and tagging data samples that are generally used to train machine learning (ML) models. Simply put, data labeling provides ML models with context to learn from [1]. The process involves tagging images, transcribing audio, or marking relevant texts. Labelled data is very important for training machine learning models as it is the only way AI algorithms can learn to make predictions or decisions.
For example, let’s
take a look at the following statement:
"The new
BRT lane on Ikorodu Road has made my daily commute to Victoria Island much
faster despite the heavy Lagos traffic."
Labels Applied:
Topic: Urban
Transportation
Sentiment:
Positive
Locations:
Ikorodu Road, Victoria Island, Lagos
Infrastructure
Type: BRT (Bus Rapid Transit)
Impact: Improved
commute time
Urban Challenge
Referenced: Traffic congestion
This labeled data helps train AI to understand urban mobility discussions and transportation infrastructure impacts in specific Lagos contexts.
Origin
The evolution of
data labeling dates as far back as the early stages of the development of
machine learning algorithms. With the increased demand for sophisticated AI
systems, the need for accurately labeled datasets became evident. This resulted
in the building of systematic data labeling processes and tools to support the
training of machine learning models.
Over time, the field of data labeling has seen significant developments, propelled by the increasing complexity of AI applications and the diverse requirements of industry-specific use cases. The historical progression of data labeling has paved the way for standardized practices and technologies that contribute to the efficiency and accuracy of machine learning algorithms [3].
Context and
Usage
Data Labelling is used for various purposes across so many sectors such as training models for disease diagnosis, treatment planning, drug discovery (healthcare); tasks such as object detection, lane recognition, and traffic sign identification (autonomous vehicles); training models for tasks like sentiment analysis, language translation, and chatbot development (natural language processing); improving customer experience through personalized recommendations (e-commerce); and training models for crop monitoring, pest detection, and yield prediction (agriculture) [2].
Why it Matters
In AI, Data
labeling is very important as it provides the ground work for machine learning
models. In other words, labeled data acts as a reference point for the models, making
it possible for them to learn and make accurate predictions. Without labeled
data, supervised learning, a popular machine learning method, would not be
possible.
Additionally, data labeling assists in improving the accuracy of AI systems. Through high-quality labeled data, machine learning models can learn more effectively and make more accurate predictions. This is crucial in sectors such as healthcare, where the accuracy of AI systems can have life-altering implications [4].
In Practice
A real-life case study of a company practicing data labelling can be seen in the case of Appen. They provide data labeling services to improve machine learning at scale. As a global leader in their field, their clients benefit from their capability to quickly deliver large volumes of high-quality data across multiple data types, including image, video, speech, audio, and text for their specific AI program needs [5].
See Also
Temperature: Controlling randomness in generated output
Tuning: Process of adjusting model parameters to optimize performance
Turing Test: Evaluating machine intelligence
References
- Pykes, K. (2024). What is Data Labeling And Why is it Necessary for AI?
- Eastgate Software. (2024). The Essential Guide to Data Labeling in AI.
- Lark Editorial Team. (2023). Data Labeling in Machine Learning.
- Netguru. (2025). Data Labeling: Artificial Intelligence Explained
- Appen. (2020). What is Data Labeling?