Garbage in, garbage out (GIGO) is a popular concept in computer programming. The GIGO principle applies in machine learning and artificial intelligence, too. If you feed your model rubbish (read: inaccurate or irrelevant data), it will turn out to be rubbish.

Properly labeling the data used to train the model is one way to ensure that it learns as intended and performs as desired. Data labeling includes tagging, annotation, transcription, classification, and moderation. Doing so allows the model to learn the context, identify patterns, and make predictions closer to reality. Proper and detailed labeling also helps where a large quantity of data is not available or feeding the model with an ever larger amount of data becomes infeasible, either due to marginal gains from significant costs or because fresh raw data is unavailable. Labeling thus reduces the need to rely on vast amounts of datasets to create an intelligent and accurate AI/ML model.

How data labeling can increase the accuracy of AI/ML models

Just as humans need parents and teachers to teach them what a thing is and what it does, AI needs human supervisors to tell them the same. How fast it learns and how accurate its learning is, depend, among other factors, on the accuracy and completeness of the training datasets.

The model cannot be better than the data but proper and detailed labeling will help it to get as close to the ground truth as possible.

Accurate labels provide ground truth for the model to learn from

Data labeling provides a ground truth for machine learning models to learn by establishing a reliable reference point against which the model’s predictions and classifications can be evaluated. Labeling data accurately ensures that the model’s predictions align with the actual attributes of the data, resulting in more reliable, accurate, and trustworthy AI systems.

When the labels are accurate, the model has a better understanding of what the data means, and it can make more accurate predictions.

Data labeling enables iterative learning

Learning something by repeating it multiple times is something that humans do naturally. Machines on the other hand need to be taught how to do it with what is known as iterative machine learning. Iteration is an efficient approach that helps reach the desired end results faster and achieve greater accuracy.

Data labeling enables iterative learning by providing the machine learning model with the data it needs to learn and provides the basis for ground truth, which increases its accuracy with each iterative process. By repeatedly training the model on new data and evaluating its performance against the labeled data, areas where the model is making errors are highlighted and avoided in the subsequent iterative phases. This feedback loop helps the model learn from its previous mistakes, refining the model over time, and increasing its accuracy and reliability.

Data labeling reduces bias in the model

We want AI and ML models to be as accurate and representative of the real world as possible. But modeling a machine on the real world also runs the risk of replicating the biases that pepper the real world. Data labeling can help mitigate this and make AI and ML models not just more accurate but also more fair.

Accurately labeling data from a diverse range of sources and with input from multiple perspectives helps models learn patterns that are not skewed—or at least less skewed—towards any particular group. Labeling also helps bring to attention rare or exceptional cases that may otherwise be ignored by the model.

Creating an AI model that is unbiased (to the extent that that is possible) is paramount as AI becomes more central in our everyday lives and impacts much of what we do. It is also essential that AI models are perceived as fair so that the technology is adopted and accepted more generally.

Importance of well-labeled datasets

An AI or an ML model is only as good as the data it is trained with. And the datasets are only as useful as the labels or tags they are given. Properly labeled datasets set an objective standard against which the model is trained and its performance assessed.

Low-quality data may not just be unhelpful in improving the model, they can actively undermine its learning and performance. They make the training more inefficient; and they influence the model in making incorrect decisions.

Another factor that determines the accuracy and utility of the AI/ML model is the size of the dataset used for training the model. The more information it is fed, the better it will be at understanding and generalizing. But information is finite; and after a certain threshold, feeding the model more data may not be worth the processing power expended. Accurate labeling helps fill the gaps in the training data that machine learning models encounter and enables them to generalize, which improves their real-world performance.

Automated labeling can help scale up labeling without compromising the quality of the labeled dataset. Human-labeled data serves as the basis for truth for automated labeling algorithms to learn and replicate. The algorithm learns from the labeled data and develops the ability to assign labels based on the patterns and features they have learned. Humans can supervise the machine and fill in the gaps to improve the accuracy of the labels.

Techniques to ensure quality in data labeling

Despite the concerted efforts of humans and machines to feed AI/ML models with accurate data, data labeling is still mired in errors. According to researchers at MIT’s Computer Science and Artificial Intelligence Labs, 3.4% of all labeled datasets have errors, in the best possible scenario.

There is much room for improvement. Thankfully, quality and accuracy in data labeling can be improved with practices within our grasp. Here are a few.

Have the right people do the right task

Data labeling requires knowledge and expertise. The quality of labeled data depends heavily on the labelers, their ability to identify and classify objects, and consistently and accurately label the data. Hire experienced and skilled professionals who can handle complex labeling tasks or outsource the data labeling services to third-parties with the requisite expertise.

Use a diverse group of labelers, review and validate

Using a diverse group of labelers can provide different points of view, identify edge cases, and reduce bias in the sample data and the model. They can further check and review each other’s work which can reduce the chance of errors. Furthermore, it is important to have experienced reviewers who can double-check the labeled data for accuracy. This extra layer of review helps spot errors and inconsistencies.

Create clear labeling guidelines

Clearly define and document labeling guidelines for labelers to follow and iteratively update them to incorporate edge cases. Having well-defined labeling guidelines will ensure that all labelers understand the labeling conventions and instructions. They should outline how to deal with edge cases and ambiguous situations. This will lead to more accurate and consistent labeling.

Have proper quality control and feedback

Implement quality control mechanisms and review samples of labeled data for accuracy. Address issues, provide clarifications, and offer feedback on their performance.

Consider the right sample size

Training an AI model on a large dataset is crucial; but size should not come at the cost of quality. Balance the trade-off between time, cost, and label accuracy. If the choice is between size and accuracy, choose accuracy. Prioritize thorough labeling for critical data samples while considering efficiency for non-critical cases. You can also outsource data and product labeling so that you can have both size and accuracy—and save the bandwidth of in-house data scientists allowing them to focus on their core roles.

Conclusion

Data labeling is an essential element in training AI and ML models. It significantly improves the quality of the dataset. This in turn influences the accuracy of the model. The ability of a machine learning model to accurately make predictions depends on the size and quality of the dataset used for training it.

As feeding models with ever larger datasets is not feasible—the cost of training becomes exponentially high, and there are finite data—improving the quality of the dataset by properly labeling them is imperative. Companies would do well to utilize the data labeling services that third parties provide, often at a fraction of the cost it’d take for in-house data labelers to do, with comparable, if not better, quality and accuracy.