An ontological approach to foster the convergence, interoperability and operationalization of frameworks for Trustworthy AI

TL;DR

This paper introduces AI-Ethics Ontology (AI-EO), a semantic infrastructure leveraging Web technologies to unify and operationalize diverse Trustworthy AI frameworks, demonstrated through case studies.

Contribution

It presents a novel ontology-based approach that enhances convergence, interoperability, and operationalization of Trustworthy AI frameworks using Semantic Technologies.

Findings

AI-Ethics Ontology (AI-EO) facilitates framework convergence.

Implementation based on two case studies demonstrates practical utility.

Version 1.0 is publicly available for use and adaptation.

Abstract

AI systems are consistently evolving in terms of both capability and autonomy with an holistic social impact. In this context of proliferation and fast technological evolution, the scientific community is actively engaged to assure Trustworthy AI. However, in general terms, AI safety research is significantly slower and is facing critical challenges in terms of strategy, consensus and operationalisation. This paper presents AI-Ethics Ontology (AI-EO) which, by leveraging Semantic Technologies on the Web infrastructure and ontology-based knowledge representations, provides an abstracted semantic infrastructure to foster the convergence, interoperability and operationalization of the different frameworks for Trustworthy AI. The current implementation results from the analysis of two relevant case studies to establish a dynamic development process in fact, as well as to enable its iterative evolution according to a formally-defined methodology. The version 1.0 of the Ontology is freely available and has been designed to be conceptually close to target applications, in a context of interoperability, adaptability as a natural response to change and usability.