  Machine learning models can perform well on in-distribution data but often
fail on biased subgroups that are underrepresented in the training data,
hindering the robustness of models for reliable applications. Such subgroups
are typically unknown due to the absence of subgroup labels. Discovering biased
subgroups is the key to understanding models' failure modes and further
improving models' robustness. Most previous works of subgroup discovery make an
implicit assumption that models only underperform on a single biased subgroup,
which does not hold on in-the-wild data where multiple biased subgroups exist.
  In this work, we propose Decomposition, Interpretation, and Mitigation (DIM),
a novel method to address a more challenging but also more practical problem of
discovering multiple biased subgroups in image classifiers. Our approach
decomposes the image features into multiple components that represent multiple
subgroups. This decomposition is achieved via a bilinear dimension reduction
method, Partial Least Square (PLS), guided by useful supervision from the image
classifier. We further interpret the semantic meaning of each subgroup
component by generating natural language descriptions using vision-language
foundation models. Finally, DIM mitigates multiple biased subgroups
simultaneously via two strategies, including the data- and model-centric
strategies. Extensive experiments on CIFAR-100 and Breeds datasets demonstrate
the effectiveness of DIM in discovering and mitigating multiple biased
subgroups. Furthermore, DIM uncovers the failure modes of the classifier on
Hard ImageNet, showcasing its broader applicability to understanding model bias
in image classifiers. The code is available at
https://github.com/ZhangAIPI/DIM.
