Label Skew

Label skew refers to a situation in machine learning where the distribution of labels in the training dataset does not accurately reflect the distribution of labels in real-world scenarios or the target data on which the model will be deployed. This discrepancy can lead to models that are biased towards the overrepresented classes and perform poorly on underrepresented classes, affecting their ability to generalize well to real-world data. Label skew is a common issue in datasets where certain classes are naturally more prevalent or easier to collect than others.

Addressing label skew is crucial for developing fair, accurate, and reliable AI systems, and strategies to mitigate this issue include resampling the dataset to balance class distribution, using weighted loss functions to account for class imbalance, or employing techniques like synthetic data generation to augment underrepresented classes.

In a medical diagnosis application, a dataset might contain a large number of negative (no disease) cases compared to positive (disease) cases because the disease is rare. This label skew can cause a machine learning model trained on this dataset to become overly proficient at identifying negative cases while performing poorly on the less frequent but critical positive cases. To counteract this, techniques such as oversampling the positive cases or undersampling the negative cases might be used to balance the dataset.

Another example is in facial recognition systems, where the training data might have a skew towards certain demographics, resulting in lower accuracy for underrepresented groups. Addressing label skew in such cases might involve collecting more data from these underrepresented groups or applying algorithmic adjustments to reduce bias. These examples highlight the importance of recognizing and correcting label skew to ensure that machine learning models perform effectively and equitably across diverse real-world scenarios.