In Defense of Information Leakage in Concept-based Models

The paper presents a critical reassessment of information leakage in concept-based models (CMs), arguing that some degree of leakage is beneficial for model performance in real-world scenarios where concepts are often incomplete. The authors propose an optimized training objective that allows CMs to harness "benign leakage," enhancing accuracy and intervenability without compromising interpretability. This perspective is significant for practitioners as it challenges the prevailing view on leakage and suggests new strategies for developing more effective CMs in practical applications.