Explainable Neural Rule Learning
Shaoyun Shi, Yuexiang Xie, Zhen Wang, Bolin Ding, Yaliang Li, Min Zhang · Proceedings of the ACM Web Conference 2022 · 2022
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.