1. FAIR Ontologies for Transparent and Accountable AI: A Hospital Adverse Incidents Vocabulary Case Study
- Author
-
Annalina Caputo, Maryam Basereh, and Rob Brennan
- Subjects
Artificial intelligence ,Vocabulary ,Knowledge management ,Relation (database) ,Computer science ,business.industry ,media_common.quotation_subject ,computer.file_format ,Ontology (information science) ,Semantics ,Transparency (behavior) ,Data governance ,Accountability ,Simple Knowledge Organization System ,FAIR ontologies ,FAccT (Fairness, Accountability, Transparency) ,Measurement ,Hospitals ,OWL ,Ontologies ,Metadata ,FAIR Principles ,Data Governance ,business ,computer ,media_common - Abstract
In this paper, the relation between the FAIR (Findable, Accessible, Interoperable, Reusable) ontologies and accountability and transparency of ontology-based AI systems is analysed. Also, governance-related gaps in ontology quality evaluation metrics were identified by examining their relation with FAIR principles and FAcct (Fairness, Accountability, Transparency) governance aspects. A simple SKOS vocabulary, titled "Hospital Adverse Incidents Classification Scheme" (HAICS) has been used as a use case for this study. Theoretically, we found that there is a straight relation between FAIR principles and FAccT AI, which means that FAIR ontologies enhance transparency and accountability in ontology-based AI systems. We suggest that "FAIRness" should be assessed as one of the ontology quality evaluation aspects.
- Published
- 2021