  Recent years have seen advances on principles and guidance relating to
accountable and ethical use of artificial intelligence (AI) spring up around
the globe. Specifically, Data Privacy, Accountability, Interpretability,
Robustness, and Reasoning have been broadly recognized as fundamental
principles of using machine learning (ML) technologies on decision-critical
and/or privacy-sensitive applications. On the other hand, in tremendous
real-world applications, data itself can be well represented as various
structured formalisms, such as graph-structured data (e.g., networks),
grid-structured data (e.g., images), sequential data (e.g., text), etc. By
exploiting the inherently structured knowledge, one can design plausible
approaches to identify and use more relevant variables to make reliable
decisions, thereby facilitating real-world deployments.
