Data-Driven Analysis of Intersectional Bias in Image Classification: A Framework with Bias-Weighted Augmentation
This addresses fairness issues in image classification for underrepresented groups, offering an incremental improvement over existing bias mitigation methods.
The paper tackled intersectional biases in image classification by proposing a framework and a novel data augmentation strategy, resulting in up to 24 percentage point accuracy improvements for underrepresented subgroups and a 35% reduction in fairness disparities.
Machine learning models trained on imbalanced datasets often exhibit intersectional biases-systematic errors arising from the interaction of multiple attributes such as object class and environmental conditions. This paper presents a data-driven framework for analyzing and mitigating such biases in image classification. We introduce the Intersectional Fairness Evaluation Framework (IFEF), which combines quantitative fairness metrics with interpretability tools to systematically identify bias patterns in model predictions. Building on this analysis, we propose Bias-Weighted Augmentation (BWA), a novel data augmentation strategy that adapts transformation intensities based on subgroup distribution statistics. Experiments on the Open Images V7 dataset with five object classes demonstrate that BWA improves accuracy for underrepresented class-environment intersections by up to 24 percentage points while reducing fairness metric disparities by 35%. Statistical analysis across multiple independent runs confirms the significance of improvements (p < 0.05). Our methodology provides a replicable approach for analyzing and addressing intersectional biases in image classification systems.