The Trustworthy AI Requirements (TAIR)
Understanding the degree to which standards compliance will deliver regulatory compliance for AI remains a complex challenge. The Trustworthy AI Requirements (TAIR) ontology offers a simple and repeatable mechanism for extracting and sharing the terms and concepts relevant to normative statements in the legal and standards texts into open knowledge graphs. The TAIR ontology provides the elements to describe requirements and concepts associated with a specific ISO standard. This representation is used to assess the adequacy of standards conformance to regulatory compliance and thereby provide a basis for identifying areas where further technical consensus development in trustworthy AI value chains will be required to achieve regulatory compliance.
The TAIR ontology is part of the project "Term and Concept Catalogue for Ethical AI Data Governance", whose objective is to generate a semantically indexed representation of the relevant existing proposed national and EU regulations to support semantic annotation and referencing in support of other analyses and to improve their accessibility to non-legal data and AI governance.
This project has received funding as a research gift from Meta and is supported by the Science Foundation Ireland under Grant Agreement No 13/RC/2106_P2 at the ADAPT SFI Research Centre and the European Union’s Horizon 2020 Marie Skłodowska-Curie grant agreement No 813497 for the PROTECT ITN.
Restricted access to the ontology
(non-description provided for requirements from copyrighted legal documents).
Full access to the ontology
Julio Noe Hernandez Torres
To cite this work, please reference: Julio Hernandez, & Dave Lewis. (2023). Open Requirements Modelling for Compliance and Conformity of Trustworthy AI. Zenodo. https://doi.org/10.5281/zenodo.7569540