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.
翻译:为促进广泛接受指导现实世界应用决策的AI系统,关键在于解决办法必须包括可靠和一体化的人类-AI系统,不仅在安全关键应用方面,如自主驾驶或医药,而且在工业和政府动态开放的世界系统中,预测模型必须是有不确定性的,并产生可靠的预测。在企业规模上部署AI的另一个关键要求是认识到将以人为本的设计纳入AI系统的重要性,使人类能够有效地使用系统,了解结果和产出,并向监督委员会解释调查结果。尽管本次专题讨论会的重点是AI系统,以提高数据质量和技术稳健性和安全性,但我们欢迎广泛界定的领域提交的材料,并讨论了解决AI可解释的模式、人类信任和伦理方面等要求的方法。