To facilitate the widespread acceptance of AI systems guiding decision-making
in real-world applications, it is key that solutions comprise trustworthy,
integrated human-AI systems. Not only in safety-critical applications such as
autonomous driving or medicine, but also in dynamic open world systems in
industry and government it is crucial for predictive models to be
uncertainty-aware and yield trustworthy predictions. Another key requirement
for deployment of AI at enterprise scale is to realize the importance of
integrating human-centered design into AI systems such that humans are able to
use systems effectively, understand results and output, and explain findings to
oversight committees.
While the focus of this symposium was on AI systems to improve data quality
and technical robustness and safety, we welcomed submissions from broadly
defined areas also discussing approaches addressing requirements such as
explainable models, human trust and ethical aspects of AI.

研究旨在提高人工智能系统的可信度和信任度，开发能够适应实际应用场景的人工智能系统，并将人类放在其中心地位，使其能够有效地使用系统，理解输出和解释结果以满足监管委员会的要求。