Concept-Centric Transformers: Enhancing Model Interpretability through Object-Centric Concept Learning within a Shared Global Workspace

Abstract

To explain "black-box" properties of AI models, many approaches, such as post hoc and intrinsically interpretable models, have been proposed to provide plausible explanations that identify human-understandable features/concepts that a trained model uses to make predictions, and attention mechanisms have been widely used to aid in model interpretability by visualizing that information. However, the problem of configuring an interpretable model that effectively communicates and coordinates among computational modules has received less attention. A recently proposed shared global workspace theory demonstrated that networks of distributed modules can benefit from sharing information with a bandwidth-limited working memory because the communication constraints encourage specialization, compositionality, and synchronization among the modules. Inspired by this, we consider how such shared working memories can be realized to build intrinsically interpretable models with better interpretability and performance. Toward this end, we propose Concept-Centric Transformers, a simple yet effective configuration of the shared global workspace for interpretability consisting of: i) an object-centric-based architecture for extracting semantic concepts from input features, ii) a cross-attention mechanism between the learned concept and input embeddings, and iii) standard classification and additional explanation losses to allow human analysts to directly assess an explanation for the model's classification reasoning. We test our approach against other existing concept-based methods on classification tasks for various datasets, including CIFAR100 (super-classes), CUB-200-2011 (bird species), and ImageNet, and we show that our model achieves better classification accuracy than all selected methods across all problems but also generates more consistent concept-based explanations of classification output.Comment: 21 pages, 9 tables, 13 figure