An Ontology for Ethical AI Principles

Tracking #: 2713-3927

This paper is currently under review
Authors: 
Andrew Harrison
Dayana Spagnuelo
Ilaria Tiddi

Responsible editor: 
Guest Editors ST 4 Data and Algorithmic Governance 2020

Submission type: 
Ontology Description
Abstract: 
The initial trickle of organisations releasing Artificial Intelligence (AI) principles documents has turned into a flood, termed the proliferation of principles, with current counts exceeding over 300 of such documents. This has led researchers to apply traditional systematic review techniques to the growing corpus of knowledge. Aims vary from meta-analytic accounts of country of origin, gender of authors, and type of organisations, to mapping principles across documents, to attempts to consolidate the vast number of principles down to a set of core authoritative principles, to authors selecting principle documents to support a research hypothesis. The commonality underlying all these efforts is traditional research techniques, which are arguably inefficient, and create static artefacts with low reusability. The Semantic Web offers a different way, an avenue to examine this proliferating body of knowledge, creating dynamic knowledge graphs, richly and more objectively connecting principles as concepts, providing enhanced semantic querying, and incorporating the existing resources from the Linked Open Data cloud. In order to achieve this, an ontology for AI principles is first required. This work presents the first ontology for Ethical AI principles (AIPO), leveraging ontology vocabularies including Dublin Core, SKOS, FOAF and DCAT2 among others, and shows its applicability through a use-case based on the OECD's AI principles set. We further discuss the benefits of AIPO, including the facilitation of systematic studies and its impact over the AI principle sets landscape.
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