Recent years have seen advances on principles and guidance relating to accountable and ethical use of artificial intelligence (AI) spring up around the globe. Specifically, Data Privacy, Accountability, Interpretability, Robustness, and Reasoning have been broadly recognized as fundamental principles of using machine learning (ML) technologies on decision-critical and/or privacy-sensitive applications. On the other hand, in tremendous real-world applications, data itself can be well represented as various structured formalisms, such as graph-structured data (e.g., networks), grid-structured data (e.g., images), sequential data (e.g., text), etc. By exploiting the inherently structured knowledge, one can design plausible approaches to identify and use more relevant variables to make reliable decisions, thereby facilitating real-world deployments.
翻译:最近几年,在与全球范围内以负责和合乎道德的方式使用人工智能(AI)相关的原则和指导方面取得了进展,具体来说,数据隐私、问责、可解释性、强性和理性被广泛承认为在决策关键和/或隐私敏感应用方面使用机器学习技术的基本原则,另一方面,在巨大的现实应用中,数据本身可以作为各种结构化的形式主义,如图表结构数据(例如网络)、电网结构数据(例如图像)、顺序数据(例如文本)等,被广泛承认为在决策关键和/或隐私敏感应用方面使用机器学习技术的基本原则。另一方面,在巨大的现实应用中,数据本身可以被充分体现为各种结构化的形式主义,如图表结构化数据(例如网络)、电网结构数据(例如图像)、顺序数据(例如文本)等。通过利用固有的结构化知识,人们可以设计合理的方法,确定和使用更相关的变量来作出可靠的决定,从而便利实际世界的部署。