Today’s interconnected world presents unique challenges for intelligent systems in processing and integrating diverse data modalities, including text, audio, and visual data. However, traditional closed-world paradigms can fall short when faced with unseen classes and novel scenarios, which frequently emerge in complicated real-world environments. We propose the consideration of open-world learning as a way to build intelligent systems that are highly adaptable while also being robust and trustworthy, capable of tackling highly dynamic and creative tasks. Here, the integration of privacy-preserving techniques is crucial as data sources expand, particularly in high-stakes applications such as autonomous navigation systems for public safety. These systems must discern and adapt to evolving traffic patterns, weather conditions, and user behaviors in real time, underscoring the necessity of continuous learning and resilience against adversities. By exploring these critical challenges, this workshop aims to foster discussions that advance the development of trustworthy, multi-modal systems capable of thriving in open-world contexts.