  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.
