In the field of artificial intelligence, data quality and diversity play a fundamental role in the performance of machine learning models. However, the challenges associated with data annotation, such as label skew and data scarcity, often complicate this process.
Let's start with a few definitions: label skew manifests itself as an unbalanced distribution of labels in a dataset, which can impair model training and distort results. Data scarcity, on the other hand, limits a model's ability to generalize effectively.
💡 These two obstacles represent a major double challenge for AI practitioners, who are looking to create robust and reliable systems. In this article, and as usual, we offer a few insights to help you better grasp these concepts!
What is the label skew and why does it pose a problem in data annotation?
The label skew refers to an imbalance in the distribution of labels within an annotated dataset. This means that some categories or classes are over-represented compared to others, which can distort the learning of artificial intelligence (AI) models.
For example, in an image classificationif the majority of images belong to a single category (such as dogs) and other categories (such as cats or birds) are very poorly represented, the model will be biased in favor of the dominant class.
This problem becomes particularly significant in data annotation, as AI models depend on the quality and diversity of the data to generalize well. In the case of label skewthe model risks overlearning the characteristics of the over-represented class, resulting in poor performance on less frequent classes. This can be problematic for critical applications where the balance between classes is essential (such as the detection of rare diseases in healthcare or the classification of anomalies in security). In addition, label skew can be particularly problematic for certain specific use cases, such as those involving ecological data or medical diagnostics, where precise measurements are essential.
💡 The label skew makes data processing and annotation work more complex, as it requires adjustments to rebalance classes or use special techniques (such as oversampling or undersampling) to mitigate the impact of imbalance on model performance.
What are the common causes of label skew in datasets?
Common causes of label skew in datasets are often linked to the nature of the data collected and the biases inherent in its source. Here are some of the main causes:
Natural imbalance in the data
Some classes or categories are naturally more frequent than others in the real world. For example, in fraud or disease detection tasks, fraudulent cases or rare diseases often represent a small proportion of the available data, creating an imbalance.
Data collection biases
The method of collection may result in label skew if certain classes are easier to collect or are collected disproportionately. For example, a dataset of images taken in an urban environment might over-represent vehicles or people and under-represent wildlife or natural scenes. Similarly, certain items such as pants in fashion data may be over-represented due to specific collection methods.
Limiting annotation resources
In some situations, expert or time-consuming manual annotations may not cover all categories fairly. This can lead to label skew if certain classes are more expensive to annotate (due to lack of available data, or because annotating certain complex shapes is more time-consuming).
Data filtering
During the data cleansing or filtering process, some classes may be disproportionately eliminated or reduced in number, creating an imbalance.
Seasonality or temporality
In some types of data, such as e-commerce or social network data, certain classes may be influenced by seasonal or temporary events. For example, during a sales period, a specific product category might be over-represented in relation to others.
Social or cultural bias
Biases introduced by users or annotators annotators themselves can also cause label skew. For example, in image recognition tasks, objects or people belonging to certain cultures or ethnic groups may be under-represented in the data.
These causes of label skew underline the complexity of data collection and annotation for AI, where an unaccounted-for imbalance can strongly affect model performance and generalization.
-hand-innv.png)
How does data scarcity exacerbate the label skew problem?
Data scarcity (or data scarcity) exacerbates label skew byfurther limiting the quantity and diversity of data available for training artificial intelligence models. Here's how these two problems compound each other:
Under-representation of minority classes
Less frequent classes become even rarer, making the pattern learning program difficult.
Overlearning for the dominant classes
The model specializes in over-represented classes, neglecting minority ones, which increases bias.
Inability to generalize and balance
The lack of data limits the model's ability to generalize correctly, especially for under-represented classes.
Increased bias in predictions
The combination of data sparsity and label skew reinforces bias, particularly in critical areas such as fraud or disease detection.
How to overcome data scarcity when annotating for AI?
Overcoming data scarcity when annotating for AI requires a combination of strategies aimed at increasing the amount of data available or maximizing the efficiency of existing data. Here are some of the approaches most commonly used to manage data sparsity in this context:
Synthetic data generation
A common method is to generate artificial data from existing data. The synthetic data can be created using techniques such as GANs (Generative Adversarial Networks) or data augmentation (data augmentation), for example by applying transformations (rotation, zoom, blur) to images or introducing noise into time series. This makes it possible to create more examples, while preserving the diversity and balance of the dataset.
Reuse of existing datasets for other AI products (knowledge transfer)
Knowledge transfer involves using a pre-trained model on another, similar dataset and fine-tuning it (fine-tuning) to the small amount of data available. This method makes it possible to take advantage of large existing datasets to compensate for data scarcity in a new task.
Semi-supervised annotation
In a semi-supervised approach, a small portion of the data is manually annotated, while the remaining unannotated data is used to train a model to generate predictions on this unlabeled data. This model is then refined over time, combining annotated and unannotated data to enrich the dataset.
Use ofsurrogate data
When direct data are scarce, it is sometimes possible to use indirectly related or surrogate data. For example, in the healthcare field, if data on a rare disease are insufficient, it may be useful to train a model on similar diseases, then adapt the results for the target disease.
Crowdsourcing for annotation
The crowdsourcing allows a large number of human contributions to be brought together to rapidly annotate datasets. Although this requires quality checks (as not all annotations are equal), this approach can help overcome data scarcity by increasing the volume of annotations, particularly for simple or visual tasks. However, be sure to take note of the working conditions of contributors working on your datasets: you could be in for some (nasty) surprises!
Oversampling and undersampling techniques
To alleviate data scarcity in certain classes, oversampling techniques can be used, where rare examples are duplicated or synthetically generated to balance the dataset. Conversely, sub-sampling over-represented classes can also reduce the imbalance, but this approach sometimes reduces the overall amount of data available.
Reinforcement learning with simulators
In environments where it is difficult to collect real-world data, simulators can be used to train models in virtual contexts, reducing dependence on real-world data. This method is common in fields such as robotics and video games.
Use of active learning packages
This practice involves training a model on a small amount of data, then requesting additional annotations only for those examples where the model is least confident. This optimizes the annotation process, maximizing the efficiency of available resources while reducing data scarcity.
🚀 Outsourcing to experts
When building datasets for AI, it is often necessary to call on the services of human experts to annotate complex or rare data. This method can guarantee high-quality annotations, thanks to the implementation of efficient workflows for creating and managing restricted and specialized datasets.
🪄 By combining several of these solutions, it's possible to overcome Data Scarcity and create richer, more balanced annotated datasets, improving the robustness and performance of artificial intelligence models.
Conclusion
Thelabel skew and data sparsity represent significant challenges in data annotation for artificial intelligence. Label skew, combined with limited data, can impair the performance of AI models, leading to biases and a reduced ability to generalize.
However, thanks to a variety of strategies, such as the use of synthetic data, knowledge transfer, semi-supervised learning, or access to the services of human expert services it is possible to overcome these obstacles.
These approaches maximize the efficiency of available data, and rebalance datasets to ensure more robust, high-performance models. In a field where data quality is paramount, proactive management of these challenges is essential to developing reliable and effective AI systems!
