Interpretable Concept-Based Memory Reasoning

TL;DR

This paper introduces Concept-based Memory Reasoner (CMR), a new interpretable AI model that combines neural memory and symbolic logic to improve transparency, verifiability, and accuracy in decision-making.

Contribution

The paper proposes CMR, a novel CBM that enables formal verification and human understanding of task predictions through explicit memory and symbolic rule evaluation.

Findings

CMR achieves better accuracy-interpretability trade-offs than existing CBMs.

CMR discovers logic rules consistent with ground truths.

CMR allows for rule interventions and pre-deployment verification.

Abstract

The lack of transparency in the decision-making processes of deep learning systems presents a significant challenge in modern artificial intelligence (AI), as it impairs users' ability to rely on and verify these systems. To address this challenge, Concept Bottleneck Models (CBMs) have made significant progress by incorporating human-interpretable concepts into deep learning architectures. This approach allows predictions to be traced back to specific concept patterns that users can understand and potentially intervene on. However, existing CBMs' task predictors are not fully interpretable, preventing a thorough analysis and any form of formal verification of their decision-making process prior to deployment, thereby raising significant reliability concerns. To bridge this gap, we introduce Concept-based Memory Reasoner (CMR), a novel CBM designed to provide a human-understandable and provably-verifiable task prediction process. Our approach is to model each task prediction as a neural selection mechanism over a memory of learnable logic rules, followed by a symbolic evaluation of the selected rule. The presence of an explicit memory and the symbolic evaluation allow domain experts to inspect and formally verify the validity of certain global properties of interest for the task prediction process. Experimental results demonstrate that CMR achieves better accuracy-interpretability trade-offs to state-of-the-art CBMs, discovers logic rules consistent with ground truths, allows for rule interventions, and allows pre-deployment verification.