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As researchers strive to narrow the gap between machine intelligence andhuman through the development of artificial intelligence technologies, it isimperative that we recognize the critical importance of trustworthiness inopen-world, which has become ubiquitous in all aspects of daily life foreveryone. However, several challenges may create a crisis of trust in currentartificial intelligence systems that need to be bridged: 1) Insufficientexplanation of predictive results; 2) Inadequate generalization for learningmodels; 3) Poor adaptability to uncertain environments. Consequently, weexplore a neural program to bridge trustworthiness and open-world learning,extending from single-modal to multi-modal scenarios for readers. 1) To enhancedesign-level interpretability, we first customize trustworthy networks withspecific physical meanings; 2) We then design environmental well-beingtask-interfaces via flexible learning regularizers for improving thegeneralization of trustworthy learning; 3) We propose to increase therobustness of trustworthy learning by integrating open-world recognition losseswith agent mechanisms. Eventually, we enhance various trustworthy propertiesthrough the establishment of design-level explainability, environmentalwell-being task-interfaces and open-world recognition programs. These designedopen-world protocols are applicable across a wide range of surroundings, underopen-world multimedia recognition scenarios with significant performanceimprovements observed.

Bridging Trustworthiness and Open-World Learning: An Exploratory Neural Approach for Enhancing Interpretability, Generalization, and Robustness