Yes, data augmentation can introduce bias into machine learning models. Data augmentation involves modifying or generating new training examples to improve model generalization. While it’s often used to address data scarcity or imbalance, the techniques themselves can unintentionally reinforce or create biases if applied without careful consideration. For example, augmentations that oversample certain features or underrepresent others can skew the model’s understanding of the data distribution, leading to biased predictions.
A common example is in image classification. Suppose a dataset of vehicles contains mostly cars photographed from the front. If a developer applies rotation-based augmentation to generate side views but does this inconsistently (e.g., only for trucks), the model might learn that “trucks are often seen from the side” while cars are not. This could lead to incorrect classifications when the model encounters real-world images where cars are viewed from the side. Similarly, in text data, synonym replacement might inadvertently swap words in a way that reinforces stereotypes (e.g., replacing “nurse” with “female nurse” but not doing the same for “doctor”), amplifying gender bias in downstream tasks like occupation classification.
To mitigate bias from augmentation, developers should audit their augmentation strategies. For instance, ensure that transformations are applied uniformly across classes or demographic groups. Tools like dataset balance checks, fairness metrics, or adversarial testing can help identify unintended patterns. For example, in natural language processing, replacing all gendered pronouns during text augmentation (e.g., randomly swapping “he” and “she”) can reduce gender bias. By prioritizing representative and balanced transformations, developers can minimize the risk of introducing bias through augmentation.