@inproceedings{ead656ce0bdd41dfa688cdf507f0a1ac,
title = "Application of an ontology for model cards to generate computable artifacts for linking machine learning information from biomedical research",
abstract = "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.",
keywords = "FAIR, artificial intelligence, description logic, document engineering, inference, machine learning, model card reports, ontology, semantic web, transparency",
author = "Amith, \{Muhammad Tuan\} and Licong Cui and Kirk Roberts and Cui Tao",
note = "Publisher Copyright: {\textcopyright} 2023 ACM.; 32nd Companion of the ACM World Wide Web Conference, WWW 2023 ; Conference date: 30-04-2023 Through 04-05-2023",
year = "2023",
month = apr,
day = "30",
doi = "10.1145/3543873.3587601",
language = "English (US)",
series = "ACM Web Conference 2023 - Companion of the World Wide Web Conference, WWW 2023",
publisher = "Association for Computing Machinery, Inc",
pages = "820--825",
booktitle = "ACM Web Conference 2023 - Companion of the World Wide Web Conference, WWW 2023",
}