Benchmarking and Enhancing Disentanglement in Concept-Residual Models
Renos Zabounidis Ini Oguntola Konghao Zhao Joseph Campbell Simon Stepputtis Katia Sycara
Concept bottleneck models (CBMs) are interpretable models that first predict
a set of semantically meaningful features, i.e., concepts, from observations
that are subsequently used to condition a downstream task. However, the model's
performance strongly depends on the engineered features and can severely suffer
from incomplete sets of concepts. Prior works have proposed a side channel -- a
residual -- that allows for unconstrained information flow to the downstream
task, thus improving model performance but simultaneously introducing
information leakage, which is undesirable for interpretability. This work
proposes three novel approaches to mitigate information leakage by
disentangling concepts and residuals, investigating the critical balance
between model performance and interpretability. Through extensive empirical
analysis on the CUB, OAI, and CIFAR 100 datasets, we assess the performance of
each disentanglement method and provide insights into when they work best.
Further, we show how each method impacts the ability to intervene over the
concepts and their subsequent impact on task performance.