More than 80% of AI project time is spent collecting, cleaning, and standardizing data [Source: hbr] for preparing AI/ML algorithms. When it comes to training datasets, the popular saying stands true – Garbage in, Garbage Out.
The quality of training data fed to the ML algorithms has a profound impact on the model’s performance. It also determines the time and effort required to train these models. In this article, we’ll explore the significance of maintaining quality in data annotation and practical methods to ensure it.
Why is the quality of annotated data so important?
AI and ML algorithms perform accurately and are better equipped to solve complex problems when fed with high-quality data. But what makes training datasets high-quality?
The qualitative aspects of data encompass various factors:
- Accuracy
- Consistency
- Completeness
- Relevancy
- Duplicacy
Maintaining the quality aspect related to these factors can help in building reliable AI/ML datasets [Source: Valleyai] that can be used to train AI models. Here’s why high-quality training data is important.
Accurate Training: Machine learning models learn from the labeled input data during training. Incorrect or inconsistent training datasets can lead to inaccurate training of these models. High-quality data leads to better pattern recognition and more precise results in machine learning algorithms.
Generalization: Well-annotated datasets help models generalize better to unseen data. If the annotations are accurate and representative of real-world scenarios, the model is more likely to perform well on new and diverse data.
Bias Mitigation: Biased training data can lead to unfair discriminatory AI outcomes. AI/ML algorithms are designed to replicate the existing biases in data and make predictions accordingly. Biased AI applications can cause severe issues, particularly when they are applied in automated operations and facial recognition.
Complex Problem Solving: Complex tasks often require nuanced annotations. If the assigned tags lack depth and accuracy, the models will struggle to understand intricate patterns, hindering their ability to solve complex problems effectively.
How poor-quality training data can impact an AI model’s performance?
Impact of faulty training data on AI models
Training AI/ML applications on poor-quality data can lead to inaccurate outcomes. Let’s explore some of them:
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Reduced model performance
AI models heavily rely on the patterns and information present in the training data. If the data contain inaccuracies or errors, the model will learn from them and make unreliable predictions that will eventually lead to reduced performance of AI systems.
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Bias and discrimination
If the training data is skewed toward specific demographics or contains discriminatory information, the AI model will reflect these biases in the output, which can result in unfair predictions.
Example: Amazon’s experimental hiring tool once became biased toward male candidates because it was trained on data that was heavily skewed toward male applicants.
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Unreliable predictions
Inaccurate training datasets can create faulty algorithms and result in unreliable predictions and outputs of the AI system. In scenarios involving medical diagnosis or the operation of autonomous vehicles, flawed AI predictions can have severe consequences and put lives at risk.
For example, an autonomous vehicle system trained on inaccurate road data can interpret a ‘truck’ as a ‘car’ and might make incorrect decisions and risk passenger safety.
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Increased cost and effort
Using low-quality data for training can result in poor model performance, necessitating additional rounds of training and more extensive data collection efforts. This leads to increased costs and effort to achieve the desired accuracy and reliability.
Tips to ensure High-Quality data Annotation
Incorporating the tips discussed in this section into your data annotation workflow can significantly contribute to the creation of high-quality labeled datasets and enhance the performance of AI models.
1. Provide clear Annotation Guidelines
Help data annotators perform effectively by giving them clear guidelines. Show them examples of both correct and incorrect annotations. Also, explain any specific instructions that are related to the subject. So that they understand what makes for high-quality annotations.
2. Train and Educate Annotators
Even with clear guidelines, annotators need appropriate knowledge sharing to perform high-quality data annotation. Provide training sessions to familiarize annotators with the guidelines, the tools they will use, and any specific domain knowledge required. Regular feedback and communication can help address questions and ensure annotators are following the guidelines accurately.
3. Assign different Annotators on the same data
This is called a consensus pipeline where multiple annotators are assigned the task of tagging the same data, and correctly labeled datasets are selected based on the consensus-based determination of accurate annotation to eliminate bias.
For instance, if four annotators designate a vehicle as a “car” and one annotator labels it as a “truck,” the consensus leans strongly toward the accuracy of the three “car” annotations, allowing the “truck” label to be dismissed. This tactic entails having several individuals label the same data instance, facilitating a collective agreement on the accurate annotation.
4. Use specialized Annotation tools
There are various data labeling tools available, ranging from simple text annotation tools to more complex image or video annotation platforms. Choose tools that align with your data type, project goals, and annotation requirements. Specialized labeling tools often have features like real-time collaboration, version control, and quality control mechanisms, which can streamline the annotation process and help maintain quality.
5. Implement Quality Control checks
Establish a mechanism to oversee and assess the quality of annotations. Implement regular assessments, random checks, or comparisons against a trusted reference training dataset. Offer feedback to annotators and promptly address any concerns that emerge. This approach ensures the accuracy and consistency of the assigned labels, contributing to the overall success of your annotation project.
6. Use gold Standard Annotations
This is an effective method to evaluate the accuracy of labeled data. Annotated datasets are compared to benchmark sets, often called ‘gold standards’ with known answers. Data annotators are recommended to compare their labeled data with the gold sets and track the data quality over time.
Final Takeaway
Data annotation quality is crucial for the effective functioning of artificial intelligence (AI) in machines and applications. Obtaining accurate and well-structured data is challenging but necessary for developing reliable, efficient, and solution-focused AI/ML models. One effective approach to ensure data quality is Human in the Loop (HITL), where human data experts work in collaboration with machine learning models to enhance accuracy.
But again, it takes people — lots of them — to provide the required data support for the ideal functioning of AI-based applications. This is where outsourcing data annotation becomes a viable option. Outsourcing data labeling tasks not only speeds up the training process but also grants access to expertise and scalable solutions. It’s a cost-effective option that helps businesses in enhancing their operations and harnessing the potential of AI and machine learning to their advantage.
