  The recent mass adoption of DNNs, even in safety-critical scenarios, has
shifted the focus of the research community towards the creation of inherently
intrepretable models. Concept Bottleneck Models (CBMs) constitute a popular
approach where hidden layers are tied to human understandable concepts allowing
for investigation and correction of the network's decisions. However, CBMs
usually suffer from: (i) performance degradation and (ii) lower
interpretability than intended due to the sheer amount of concepts contributing
to each decision. In this work, we propose a simple yet highly intuitive
interpretable framework based on Contrastive Language Image models and a single
sparse linear layer. In stark contrast to related approaches, the sparsity in
our framework is achieved via principled Bayesian arguments by inferring
concept presence via a data-driven Bernoulli distribution. As we experimentally
show, our framework not only outperforms recent CBM approaches accuracy-wise,
but it also yields high per example concept sparsity, facilitating the
individual investigation of the emerging concepts.
