Characterizing Dataset Bias via Disentangled Visual Concepts
Jinho Choi, Hyesu Lim, Steffen Schneider, Jaegul Choo
Dataset bias is ubiquitous in machine learning datasets. Yet, systematically identifying these biases is challenging without costly, fine-grained attribute annotations. We introduce ConceptScope, a framework for characterizing dataset bias by disentangling visual concepts using a Sparse Autoencoder. Our framework automatically discovers visual concepts present in datasets and distinguishes them into target, contextual, and bias concepts. We first validate our framework by accurately detecting six distinct types of visual concepts—object, texture, background, facial attributes, emotion, and action—achieving high accuracy on labeled datasets. We then extend our approach to discover both known biases in annotated datasets (such as CelebA) and novel biases in datasets without explicit bias annotations (such as ImageNet). Furthermore, we introduce a method to partition test data into subgroups based on the strength and presence of task-related versus bias concepts, introducing a practical use case of ConceptScope for model robustness diagnosis. Our approach leverages existing datasets without the need for additional bias annotations, providing valuable insights into how concept distributions affect model generalization under bias-induced distribution shifts.