“The model and the code for many applications are basically a solved problem. Now that the models have advanced to a certain point, we got to make the data work as well.” – Andrew Ng, the Founder of DeepLearning.AI
Have you heard of instances such as a self-driving car misses a pedestrian in an unexpected position, a medical AI application misdiagnoses a rare disease variant, or a content moderation system unfairly flags cultural expressions? The root cause is often the same: insufficient or flawed data labeling.
Despite investments in developing algorithms and expanding computing capabilities, many machine learning projects stumble due to gaps in data labeling. As datasets become larger and more complex, the challenge intensifies, as annotators must manage edge cases and mitigate biases. Addressing these issues is crucial for maintaining the data quality required for reliable machine learning models.
In this blog post, we will learn what role data annotation plays in building machine learning models and examine how organizations are navigating the challenges of scaling annotation operations while maintaining the data quality necessary.
Why Accuracy is Becoming a Major Challenge for ML Models?
According to a study conducted by MIT and Harvard, 91% of machine learning models deteriorate in performance over time. This phenomenon, known as model drift, often arises due to several issues, including:
- Evolving user behavior, including new linguistic patterns or interaction styles
- The increasing scale and complexity of data sources, making consistent labeling difficult
- Changes in the environment and external events (e.g., economic shifts, pandemics) that alter data distributions
- Data integrity problems caused by corrupted, incomplete, or inconsistent data in pipelines
New scams, such as deepfakes, are becoming increasingly common. Consider traditional machine learning models built to detect fraudulent transactions.
Such models might be good at detecting conventional fraud patterns, such as stolen credit card usage or phishing attempts, but fail to account for new types of fraudulent behaviors that differ from the original training dataset. To keep such models effective, new data must be annotated, such as audio and video suspected of being deepfakes, bot-driven interactions, etc.
This real-world example highlights the necessity for continuous, accurate data labeling for machine learning models.
The Need for High-Quality Data Labeling in Building Effective ML Models
With growing challenges in the deployment of ML models, accurate data labeling is important for:
1. Enabling Pattern Recognition and Classification
By adding meaningful tags or labels across raw data such as images, text, audio, and video, data annotators provide context to the machine learning models. Without such labels, raw data is just unstructured information that models cannot interpret or learn from effectively.
Take the example of customer service chatbots. Annotators label user queries such as “billing issue” or “technical support”, depending on the type of problem. This allows natural language processing (NLP) models to understand the customer’s issue and respond with an appropriate answer.
2. Improving Model Accuracy
When labels correctly describe the data, the model can learn actual patterns and make predictions. If the labels are wrong or inconsistent, the model learns incorrect information. The impact includes:
- Poor prediction accuracy
- Inconsistent model behavior
- An increased rate of false positives or negatives
For example, in medical imaging, correctly marking tumor boundaries in MRI scans helps models understand the difference between healthy and cancerous tissue. Whereas, wrong labels can cause problems like:
False Negatives:
- Small or low-contrast tumors may be missed, delaying diagnosis
- Tumors with benign-like shapes can be misclassified as normal
False Positives:
- Benign lesions (like inflammation or infection) are incorrectly labeled as tumors
3. Facilitating Model Validation and Testing
Labeled data also serves as the benchmark for testing the effectiveness of the model. By comparing the model’s predictions against the annotated data, model testers can measure accuracy, detect weaknesses, and refine the model. Without this labeled data, it would be nearly impossible for companies to assess the model’s safety and reliability before deployment.
For example, in email spam detection, emails are annotated as “spam” or “not spam”. This helps testers see how well the model is working. If the model misses spam emails or wrongly marks regular emails as spam, it is clear that the model needs to be improved before it’s out in the market.
4. Reducing ML Model Bias
In a few ML models, some types of data points are overrepresented while others are scarce. This imbalance causes the model to favor certain patterns and overlook others, leading to unfair results. Using a diverse dataset for labeling makes sure that the model can handle various real-world scenarios without providing unfair outcomes.
For example, take autonomous driving systems. If the training data primarily consists of annotated images taken in clear weather conditions, the model may struggle to detect objects during rain or fog. By including a diverse range of weather scenarios, annotators help build a balanced dataset that enables the model to perform reliably and fairly in various real-world environments.
5. Improving Model Continuously
As new data emerges over time, updating training datasets with new labeled information is essential. Fresh labels help models learn new patterns and handle emerging scenarios effectively. Without regular updates, models risk becoming outdated, leading to reduced performance and unreliable results in real-world applications.
For example, take a voice assistant. New slang words like “ghosting” (meaning suddenly ignoring someone) may become popular over time. Continuously labeling and adding these new expressions helps the model understand what users mean, keeping the assistant relevant in everyday conversations.
Three Effective Approaches for Implementation of Large-Scale Data Annotation
As data volume grows and annotation tasks become more complex, organizations must choose how to execute annotations to balance cost, quality, and scalability. There are three primary approaches as discussed below:
1. In-House Annotation Teams
Some companies prefer to keep data labeling in-house, especially when dealing with sensitive information (healthcare records, financial data, or personally identifiable information). Keeping it internal gives them more control over how data is handled and lets them manage data labeling quality more closely.
That said, having an in-house team has a big drawback. Wondering what? It takes a lot of time and money to hire, train, and retain skilled annotators. This can quickly raise a company’s costs.
2. Crowdsourcing Platforms
The second option for companies is crowdsourcing platforms like Amazon Mechanical Turk and Twine AI. These platforms provide access to a large, distributed workforce across the world. This approach is preferable for straightforward tasks or when large volumes of data need rapid labeling.
That said, quality control is often the biggest challenge here. Skill levels vary a lot across contributors, so it’s important to put solid checks in place with things like validation steps and clear instructions so that the output stays consistent.
3. Outsourcing Data Annotation Services
Considering an alternative? Data annotation service providers can help with a balanced approach. Outsourcing is an ideal solution for varied reasons such as scalability, SMEs, cost reduction, and confirmed accuracy. Partnering with a service provider can help in the following ways:
- It provides scalability, allowing organizations to adjust annotation efforts as needed without long-term commitments.
- These providers typically have trained annotators and access to advanced tools that can help with subject matter expertise regardless of the data size.
- They implement robust quality assurance processes and reduce the burden of managing annotation projects internally.
Not sure whether to use crowdsourcing, outsource data labeling services, or hire your team? Start by asking yourself a few simple questions:
- How much data do you have?
- What’s your budget?
- How long will the project take?
- What kind of data are you working with?
Your answers will help you pick the best option to get good-quality annotations. Remember, accurate data is really important to get trustworthy results and avoid mistakes. So, make sure you give your system the right data to keep it working well in real-world scenarios.