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.

翻译：模型卡片报告提供对机器学习模型的透明描述，包括其评估、局限性、预期用途等信息。联邦卫生机构已对使用基于机器学习的人工智能的研究中的模型卡片报告表示兴趣。此前，我们为模型卡片报告开发了一个本体模型，以结构化和形式化这些报告。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片报告。我们讨论了未来方向及其他用例，以突出本体驱动系统支持FAIR挑战的适用性与可行性。