Amith Muhammad Tuan, Cui Licong, Roberts Kirk, Tao Cui
University of North Texas, USA.
The University of Texas Health Science Center at Houston, USA.
Proc Int World Wide Web Conf. 2023 Apr;2023(Companion):820-825. doi: 10.1145/3543873.3587601. Epub 2023 Apr 30.
Model card reports provide a transparent description of machine learning models which includes information about their evaluation, limitations, intended use, etc. Federal health agencies have expressed an interest in model cards report for research studies using machine-learning based AI. Previously, we have developed an ontology model for model card reports to structure and formalize these reports. In this paper, we demonstrate a Java-based library (OWL API, FaCT++) that leverages our ontology to publish computable model card reports. We discuss future directions and other use cases that highlight applicability and feasibility of ontology-driven systems to support FAIR challenges.
模型卡报告提供了对机器学习模型的透明描述，其中包括有关其评估、局限性、预期用途等信息。联邦卫生机构对基于机器学习的人工智能研究的模型卡报告表示出兴趣。此前，我们已经开发了一个用于模型卡报告的本体模型，以使这些报告结构化和形式化。在本文中，我们展示了一个基于Java的库（OWL API，FaCT++），该库利用我们的本体来发布可计算的模型卡报告。我们讨论了未来的方向和其他用例，这些用例突出了本体驱动系统在支持FAIR挑战方面的适用性和可行性。
