Exploring the role of labeled data in machine learning

Exploring the role of labeled data in machine learning

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If there’s one thing that has fueled the rapid progress of AI and machine learning (ML), it’s data. Without high-quality labeled datasets, modern supervised learning systems simply wouldn’t be able to perform.

But using the right data for your model isn’t as simple as gathering random information and pressing “run.” There are several underlying factors that can significantly impact the quality and accuracy of an ML model. 

If not done right, the labor intensive task of data labeling can result in bias and poor performance. The use of augmented or synthetic data may amplify existing biases or distort reality, and automated labeling techniques might increase the need for quality assurance.

Let’s explore the importance of quality labeled data in training AI models to perform tasks effectively, as well as some of key challenges, potential solutions and actionable insights.


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What is labeled data?

Labeled data is a fundamental requirement for training any supervised ML model. Supervised learning models use labeled data to learn and infer patterns, which they can then apply to real-world unlabeled information.

Some examples of the utility of labeled data include:

  • Image data: A basic computer vision model built for detecting common items around the house would need images tagged with classifications like “cup,” “dog,” “flower.” 
  • Audio data: Natural language processing (NLP) systems use transcripts paired with audio to learn speech-to-text capabilities.
  • Text data: A sentiment analysis model might be built with labeled text data including sets of customer reviews each tagged as positive, negative or neutral.
  • Sensor data: A model built to predict machinery failures could be trained on sensor data paired with labels like “high vibration” or “over temperature.”

Depending on the use case, models can be trained on one or multiple data types. For example, a real-time sentiment analysis model might be trained on text data for sentiment and audio data for emotion, allowing for a more discerning model.

The type of labeling also depends on the use case and model requirements. Labels can range from simple classifications like “cat” or “dog” to more detailed pixel-based segmentations outlining objects in images. There may also be hierarchies in the data labeling — for example, you might want your model to understand that both cats and dogs are usually household pets.

Data labeling is often done manually by humans, which has obvious drawbacks, including massive time cost and the potential for unconscious biases to manifest datasets. There are a number of automated data labeling techniques that can be leveraged, but these also come with their own unique problems.

High-quality labeled data is critically important for training supervised learning models. It provides the context necessary for building quality models that will make accurate predictions. In the realm of data analytics and data science, the accuracy and quality of data labeling often determine the success of ML projects. For businesses looking to embark on a supervised project, choosing the right data labeling tactics is essential.

Approaches to data labeling

There are a number of approaches to data labeling, each with its own unique benefits and drawbacks. Care must be taken to select the right option for your needs, as the labeling approach selected will have significant impacts on cost, time and quality.

  • Manual labeling: Despite its labor intensive nature, manual data labeling is often used due to its reliability, accuracy and relative simplicity. It can be done in-house or outsourced to professional labeling service providers.
  • Automated labeling: Methods include rule-based systems, scripts and algorithms, which can help to speed up the process. Semi-supervised learning is often employed, during which a separate model is trained on small amounts of labeled data and then used to label the remaining dataset. Automated labeling can suffer from inaccuracies — especially as the datasets increase in complexity.
  • Augmented data: Techniques can be employed to make small changes to existing labeled datasets, effectively multiplying the number of available examples. But care must be taken, as augmented data can potentially increase existing biases within the data.
  • Synthetic data: Rather than modifying existing labeled datasets, synthetic data uses AI to create new ones. Synthetic data can feature large volumes of novel data, but it can potentially generate data that does not accurately reflect reality — increasing the importance of quality assurance and proper validation.
  • Crowdsourcing: This provides access to human annotators but introduces challenges around training, quality control and bias.
  • Pre-labeled datasets: These are tailored to specific uses and can often be used for simpler models.

Challenges and limitations in data labeling

Data labeling presents a number of challenges due to the need for vast amounts of high-quality data. One of the primary concerns in AI research is the inconsistent nature of data labeling, which can significantly impact the reliability and effectiveness of models. These include:

  • Scalability: Manual data labeling requires significant human efforts, which severely impact scalability. Alternatively, automated labeling and other AI-powered labeling techniques can quickly become too expensive or result in low quality datasets. A balance must be found between time, cost and quality when undertaking a data labeling exercise.
  • Bias: Whether conscious or unconscious, large datasets can often suffer from some form of underlying bias. These can be combated by using thoughtful label design, diverse teams of human annotators and thorough checking of trained models for underlying biases.
  • Drift: Inconsistencies between individuals as well as changes over time can result in performance reduction as new data shifts from the original training dataset. Regular human training, consensus checks and up-to-date labeling guidelines are important for avoiding label drift.
  • Privacy: Personally identifiable information (PII) or confidential data requires secure data labeling processes. Techniques like data redaction, anonymization and synthetic data can manage privacy risks during labeling.

There is no one size fits all solution for efficient large-scale data labeling. It requires careful planning and a healthy balance, considering the various dynamic factors at play. 

The future of data labeling in machine learning

The progression of AI and ML is not looking to slow down anytime soon. Alongside this is the increased need for high-quality labeled datasets. Here are some key trends that will shape the future of data labeling:

  • Size and complexity: As ML capabilities progress, datasets that train them are getting bigger and more complex.
  • Automation: There is an increasing trend towards automated labeling methods which can significantly enhance efficiency and reduce costs involved with manual labeling. Predictive annotation, transfer learning and no-code labeling are all seeing increased adoption in an effort to reduce humans in the loop.
  • Quality: As ML is applied to increasingly important fields such as medical diagnosis, autonomous vehicles and other systems where human life might be at stake, the necessity for quality control will dramatically increase.

As the size, complexity and criticality of labeled datasets increases, so too will the need for improvement in the ways we currently label and check for quality.

Actionable insights for data labeling 

Understanding and choosing the best approach to a data labeling project can have a huge impact on its success from a financial and quality perspective. Some actionable insights include:

  • Assess your data: Identify the complexity, volume and type of data you are working with before committing to any one labeling approach. Use a methodical approach that best aligns with your specific requirements, budget and timeline.
  • Prioritize quality assurance: Implement thorough quality checks, especially if automated or crowdsourced labeling methods are used.
  • Take privacy considerations: If dealing with sensitive or PII, take precautions to prevent any ethical or legal issues down the line. Techniques like data anonymization and redaction can help maintain privacy.
  • Be methodical: Implementing detailed guidelines and procedures will help to minimize bias, inconsistencies and mistakes. AI powered documentation tools can help track decisions and maintain easily accessible information.
  • Leverage existing solutions: If possible, utilize pre-labeled datasets or professional labeling services. This can save time and resources. When looking to scale data labeling efforts, existing solutions like AI powered scheduling could help optimize the workflow and allocation of tasks.
  • Plan for scalability: Consider how your data labeling efforts will scale with the growth of your projects. Investing in scalable solutions from the start can save effort and resources in the long run.
  • Stay informed: Stay up to speed on emerging trends and technologies in data labeling. Tools like predictive annotation, no-code labeling and synthetic data are constantly improving making data labeling cheaper and faster. 

Thorough planning and consideration of these insights will enable a cheaper and smoother operation, and ultimately, a better model.

Final thoughts

The integration of AI and ML into every aspect of society is well under way, and datasets needed to train algorithms continue to grow in size and complexity.

To maintain the quality and relative affordability of data labeling, continuous innovation is needed for both existing and emerging techniques.

Employing a well-thought-out and tactical approach to data labeling for your ML project is critical. By selecting the right labeling technique for your needs, you can help ensure a project that delivers on requirements and budget.

Understanding the nuances of data labeling and embracing the latest advancements will help to ensure the success of current projects, as well as labeling projects to come. 

Matthew Duffin is a mechanical engineer and founder of rareconnections.io.


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