Targeted Data Augmentation for bias mitigation
Agnieszka Mikołajczyk-Bareła Maria Ferlin Michał Grochowski
The development of fair and ethical AI systems requires careful consideration
of bias mitigation, an area often overlooked or ignored. In this study, we
introduce a novel and efficient approach for addressing biases called Targeted
Data Augmentation (TDA), which leverages classical data augmentation techniques
to tackle the pressing issue of bias in data and models. Unlike the laborious
task of removing biases, our method proposes to insert biases instead,
resulting in improved performance. To identify biases, we annotated two diverse
datasets: a dataset of clinical skin lesions and a dataset of male and female
faces. These bias annotations are published for the first time in this study,
providing a valuable resource for future research. Through Counterfactual Bias
Insertion, we discovered that biases associated with the frame, ruler, and
glasses had a significant impact on models. By randomly introducing biases
during training, we mitigated these biases and achieved a substantial decrease
in bias measures, ranging from two-fold to more than 50-fold, while maintaining
a negligible increase in the error rate.