AS-XAI: Self-Supervised Automatic Semantic Interpretation for CNN

Abstract

Interpretable Machine Learning

Interpretable machine learning is essential for building trustworthy AI systems. Automated Semantically Interpretable AI (AS-XAI) extracts the common semantic feature space of diverse data samples and combines this feature space with a sensitivity analysis of neural networks in each semantic space to understand the networks’ decision-making processes. AS-XAI leverages the model’s understanding of common semantics in existing data to enable a wide range of fine-grained and scalable real-world applications. This approach allows for comprehensive semantic conceptual interpretations of out-of-distribution hybrids as well as species that are difficult for humans to recognize. See article number 2400359 by Changqi Sun, Hao Xu, Yuntian Chen, and Dongxiao Zhang.