All machine learning systems are trained on data. Data is fuel for these systems. An ML system’s performance is totally based on the data it is trained on. Good data will produce a good model, and poor data will produce a poor model. In the case of the former, it can yield state-of-the-art results. But we must remember that the data we feed into these systems must be well-structured and labeled. This is where data labeling plays an important role while preparing the data.
Labeling data is important in all supervised learning tasks. This may include image recognition, text classification, et cetera. But labeling data manually is a time-consuming process. It is expensive and prone to human error. This is where auto-labeling comes into the picture. It is a promising alternative that can save time and money and efficiently automate a huge amount of them without human intervention.
In this blog, we will understand the importance and the benefits of auto-labeling. We will also look at some of the challenges auto-labeling poses.
What is Auto-labeling?
Auto-labeling or automatic data labeling is like manual data labeling, but instead of human labeling each and every sample of the data, trained algorithms do it automatically. Generally, it involves training an ML model to learn the relationship between the input sample, i.e., the original image with its respective output, in this case, a label. Once the model is fully trained, it labels new and unseen data.
Sometimes the trained model is tuned using reinforcement learning with human feedback or RLHF. RLHF can be an ongoing process when the system is labeling data. When human experts validate the data, they can correct or even add missing labels if the system fails to add them. When the labels are corrected or newly added, the system takes them as feedback and improves itself. This happens continuously, thus improving the system effectively.
The process of auto-labeling typically involves the following steps:
- Collecting large amounts of data
- Dividing the dataset into training and testing subsets.
- Train a machine learning model on the training set using manual labeling.
- Use the trained model to automatically label the test set.
- Validate the accuracy of the auto-labeled data using a subset for manual verification.
- Note: In some cases, RLHF is leveraged in the evaluation pipeline. Here, the samples that are incorrect and mislabeled are corrected by a human expert, and feedback is given to the system. Simultaneously, these samples are sent back to the labeling algorithm such that it will correctly label them.
Benefits of Auto Labeling for ML
Auto labeling provides several benefits for machine learning, including:
- Faster and More Efficient Labeling: Auto labeling allows for quickly labeling large volumes of data without human intervention. This saves time and resources and allows ML projects to progress at a faster pace. In many cases, for example, in self-driving cars, the labeling process can be a part of the entire ML pipeline, where once a sufficient amount of data is labeled, it can be pushed into the data repository for training purposes.
- Increased Accuracy and Consistency: Auto labeling also provides accurate and consistent labeling results, as it eliminates the potential for human error and subjectivity. This improves the overall quality of the ML model and reduces the risk of biased results.
But it is worth noting that the accuracy depends on the data the model was trained on. If the data isn’t well-curated, it can produce false positives, raising problems in the downstream tasks.
- Cost-Effective Labeling: Manual labeling can be time-consuming, leading to increased cost. When humans are involved, various tools are also involved. Auto labeling significantly reduces the cost of data labeling, as it eliminates the need for human labelers. This makes ML projects more cost-effective and accessible to organizations with limited budgets.
- Ability to Handle Large Datasets: Auto labeling is able to handle large and complex datasets that would be difficult and time-consuming to label manually. This allows ML models to be trained on more data, which can improve their accuracy and performance.
Most manual systems are fixed or only support a small variety of label sizes. But a variety of label sizes can be accommodated by automatic systems. This allows you to effortlessly feed the data of any sample size into the system and stay assured that it won’t get tired or fatigued.
- Reduced Human Error: Automated data labeling eliminates the potential for human error in the labeling process, which can occur due to fatigue, inconsistency, or bias. This improves the accuracy and reliability of the ML model.
- Improved Productivity and Workflow: Auto labeling improves the productivity and workflow of ML projects, as it allows data scientists to focus on more complex tasks, such as model selection and optimization, rather than spending time on manual labeling.
Challenges with Auto Labeling
However, there are several challenges and considerations that must be taken into account. In this response, we will discuss four key challenges with auto labeling: limitations and constraints, ethical considerations, quality control and validation, and transparency and accountability.
- The algorithms trained to label the data are only good as the data fed into it. This is the main concern because the accuracy and performance of these labeling algorithms largely depend on the trained data. If the data is not curated properly, the algorithm may result in inaccurate labeling, false positives, and sometimes incomplete labeling. This, in turn, will affect the downstream tasks.
- Ensuring the accuracy and quality of auto-labeled data is essential to prevent negative impacts and maintain credibility. However, quality control and validation can be challenging with auto-labeling, particularly if the labeling task is complex or the algorithm is not interpretable. It may be necessary to employ human experts to manually review and verify the accuracy of auto-labeled data, which can be time-consuming and costly.
- These algorithms can generate false positives, as mentioned before. To tackle this, humans are generally assigned in the loop. When humans are involved, it makes the process much slower than it needs to be.
- Another challenge is interpretability and transparency. Since these algorithms are complex, it is hard to understand why they made such a decision. This can essentially raise concerns about bias and discrimination. For instance, in the case of radiology, the algorithm can classify an unknown structure in the CT scan as a malign cell, but the same structure may or may not be a malign cell. Now the question remains how did the algorithm make such a decision?
We are witnessing how rapidly the AI industry is evolving. With each new day, new and state-of-the-art algorithms are being developed and released. All these algorithms need well-curated and well-labeled datasets. Automated data labeling comes into the picture to aid developers and researchers with well-curated and well-labeled datasets.
Auto-labeling is pushing the boundaries of AI systems as it delivers labeled data quickly, offers consistency, saves money and time, and reduces error significantly compared to human or manual labeling. But like any other AI system, it still lacks accuracy and reliability; this is where a human expert plays a vital role.
In any case, we are moving into a world of automation where these systems will continuously improve and offer accurate and reliable datasets.