论文标题
可解释的人工智能：系统评价
Explainable Artificial Intelligence: a Systematic Review
论文作者
论文摘要
在过去的几年中，可解释的人工智能（XAI）经历了显着增长。这是由于机器学习的广泛应用，尤其是深度学习，这导致了高度准确的模型的发展，但缺乏解释性和解释性。已经提出，开发和测试了大量解决此问题的方法。这项系统的综述通过将这些方法与分层分类系统聚集在一起，从而有助于知识的体系：回顾文章，理论和概念，方法及其评估。它还总结了XAI的最新技术，并建议未来的研究方向。
Explainable Artificial Intelligence (XAI) has experienced a significant growth over the last few years. This is due to the widespread application of machine learning, particularly deep learning, that has led to the development of highly accurate models but lack explainability and interpretability. A plethora of methods to tackle this problem have been proposed, developed and tested. This systematic review contributes to the body of knowledge by clustering these methods with a hierarchical classification system with four main clusters: review articles, theories and notions, methods and their evaluation. It also summarises the state-of-the-art in XAI and recommends future research directions.
