Explainable Neural Rule Learning
Shaoyun Shi, Yuexiang Xie, Zhen Wang, Bolin Ding, Yaliang Li, Min Zhang
Abstract
Although neural networks have achieved great successes in various machine learning tasks, people can hardly know what neural networks learn from data due to their black-box nature. The lack of such explainability is one of the limitations of neural networks when applied in domains, e.g., healthcare and finance, that demand transparency and accountability. Moreover, explainability is beneficial for guiding a neural network to learn the causal patterns that can extrapolate out-of-distribution (OOD) data, which is critical in real-world applications and has surged as a hot research topic.
Topics & Concepts
Artificial neural networkTransparency (behavior)Computer scienceBlack boxDeep neural networksAccountabilityArtificial intelligenceMachine learningDeep learningComputer securityPolitical scienceLawExplainable Artificial Intelligence (XAI)Machine Learning and Data ClassificationMachine Learning in Healthcare