OCCAM: Open-set Causal Concept explAnation and Ontology induction for black-box vision Models
For researchers and practitioners needing interpretability of black-box vision models, OCCAM provides a method to discover and causally explain visual concepts without model access, offering richer global insights.
OCCAM introduces a framework for open-set causal concept explanation and ontology induction in black-box vision models, enabling both local explanations and global structured ontologies. Experiments on Broden and ImageNet-S show improved explanation quality over per-image attribution methods.
Interpreting the decisions of deep image classifiers remains challenging, particularly in black-box settings where model internals are inaccessible. We introduce OCCAM, a framework for open-set causal concept explanation and ontology induction in vision models. OCCAM discovers visual concepts in an open-set manner, localizes them via text-guided segmentation, and performs object-level interventions by removing concepts to measure changes in class confidence, estimating each concept's causal contribution. Beyond local explanations, OCCAM aggregates interventional evidence across a dataset to induce a structured concept ontology that captures how classifiers globally organize visual concepts. Reasoning over this ontology reveals consistent dependencies between concepts, exposes latent causal relations, and uncovers systematic model biases. Experiments on Broden and ImageNet-S across multiple classifiers show that OCCAM improves explanation quality in open-set black-box settings while providing richer global insight than per-image attribution methods.