As AI systems have obtained significant performance to be deployed widely in our daily live and human society, people both enjoy the benefits brought by these technologies and suffer many social issues induced by these systems. To make AI systems good enough and trustworthy, plenty of researches have been done to build guidelines for trustworthy AI systems. Machine learning is one of the most important parts for AI systems and representation learning is the fundamental technology in machine learning. How to make the representation learning trustworthy in real-world application, e.g., cross domain scenarios, is very valuable and necessary for both machine learning and AI system fields. Inspired by the concepts in trustworthy AI, we proposed the first trustworthy representation learning across domains framework which includes four concepts, i.e, robustness, privacy, fairness, and explainability, to give a comprehensive literature review on this research direction. Specifically, we first introduce the details of the proposed trustworthy framework for representation learning across domains. Second, we provide basic notions and comprehensively summarize existing methods for the trustworthy framework from four concepts. Finally, we conclude this survey with insights and discussions on future research directions.

随着人工智能系统取得了显着的性能并在我们的日常生活和人类社会中广泛部署，人们在享受这些技术带来的好处的同时，也遭受了这些系统引发的许多社会问题。为了使人工智能系统足够好且值得信赖，人们进行了大量的研究来建立可信赖的人工智能系统的指南。机器学习是人工智能系统最重要的部分之一，表示学习是机器学习的基础技术。如何使表示学习在实际应用中（例如跨域场景）值得信赖，对于机器学习和人工智能系统领域来说都是非常有价值和必要的。受可信人工智能概念的启发，我们提出了第一个跨领域可信表示学习框架，其中包括鲁棒性、隐私性、公平性和可解释性四个概念，以对该研究方向进行全面的文献综述。具体来说，我们首先介绍了所提出的跨领域表示学习的可信框架的细节。其次，我们从四个概念出发，给出了可信框架的基本概念并全面总结了现有的可信框架方法。最后，我们通过对未来研究方向的见解和讨论来结束本次调查。