  As researchers strive to narrow the gap between machine intelligence and
human through the development of artificial intelligence technologies, it is
imperative that we recognize the critical importance of trustworthiness in
open-world, which has become ubiquitous in all aspects of daily life for
everyone. However, several challenges may create a crisis of trust in current
artificial intelligence systems that need to be bridged: 1) Insufficient
explanation of predictive results; 2) Inadequate generalization for learning
models; 3) Poor adaptability to uncertain environments. Consequently, we
explore a neural program to bridge trustworthiness and open-world learning,
extending from single-modal to multi-modal scenarios for readers. 1) To enhance
design-level interpretability, we first customize trustworthy networks with
specific physical meanings; 2) We then design environmental well-being
task-interfaces via flexible learning regularizers for improving the
generalization of trustworthy learning; 3) We propose to increase the
robustness of trustworthy learning by integrating open-world recognition losses
with agent mechanisms. Eventually, we enhance various trustworthy properties
through the establishment of design-level explainability, environmental
well-being task-interfaces and open-world recognition programs. These designed
open-world protocols are applicable across a wide range of surroundings, under
open-world multimedia recognition scenarios with significant performance
improvements observed.
