论文标题
AAAI FSS-19：以人为本的AI：AI模型和数据程序的可信度
AAAI FSS-19: Human-Centered AI: Trustworthiness of AI Models and Data Proceedings
论文作者
论文摘要
为了促进在现实世界应用中广泛接受AI系统指导决策的广泛接受，这是解决方案构成值得信赖的，集成的人类系统系统的关键。不仅在自动驾驶或医学等安全性应用中，而且在行业和政府的动态开放世界系统中，对于预测模型来说，不确定性感知并产生值得信赖的预测至关重要。在企业量表上部署AI的另一个关键要求是实现将以人为本的设计集成到AI系统中的重要性，以便人类能够有效地使用系统，理解结果和输出，并向监督委员会解释发现。 尽管该研讨会的重点是改善数据质量和技术鲁棒性和安全性的AI系统，但我们欢迎从广义定义的领域提交的提交，还讨论了解决要求的方法，例如可解释的模型，人类的信任和AI的道德方面。
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.
