论文标题
第一届ICLR关于隐私，问责制，可解释性，鲁棒性，结构化数据的推理（PAIR^2 -struct）的国际研讨会
1st ICLR International Workshop on Privacy, Accountability, Interpretability, Robustness, Reasoning on Structured Data (PAIR^2Struct)
论文作者
论文摘要
近年来，与人工智能（AI）的责任和道德使用有关的原则和指导的进步都在全球范围内出现。具体而言，数据隐私，问责制，可解释性，鲁棒性和推理已被广泛认为是使用机器学习（ML）技术的基本原理（ML），以决策至关重要和/或对隐私敏感的应用程序。另一方面，在巨大的现实应用应用中，数据本身可以很好地表示为各种结构性形式主义，例如图形结构化数据（例如网络），网格结构的数据（例如，图像），顺序数据，顺序数据（例如，文本）（例如，文本），等等，等固有的固有的知识，可以使更相关的差异来识别和使用相关的变量，从而使可靠的变量可靠地识别出可靠的变量。现实世界的部署。
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.
