The growing number of incidents caused by (mis)using Artificial Intelligence (AI) is a matter of concern for governments, organisations, and the public. To control the harmful impacts of AI, multiple efforts are being taken all around the world from guidelines promoting trustworthy development and use, to standards for managing risks and regulatory frameworks. Amongst these efforts, the first-ever AI regulation proposed by the European Commission, known as the AI Act, is prominent as it takes a risk-oriented approach towards regulating development and use of AI within systems. In this paper, we present the AI Risk Ontology (AIRO) for expressing information associated with high-risk AI systems based on the requirements of the proposed AI Act and ISO 31000 series of standards. AIRO assists stakeholders in determining ‘high-risk’ AI systems, maintaining and documenting risk information, performing impact assessments, and achieving conformity with AI regulations. To show its usefulness, we model existing real-world use-cases from the AIAAIC repository of AI-related risks, determine whether they are high-risk, and produce documentation for the EU’s proposed AI Act.
Conforming to multiple and sometimes conflicting guidelines, standards, and legislations regarding development, deployment, and governance of AI is a serious challenge for organisations. While the AI standards and regulations are both in early stages of development, it is prudent to avoid a highly-fragmented landscape and market confusion by finding out the gaps and resolving the potential conflicts. This paper provides an initial comparison of ISO/IEC 42001 AI management system standard with the EU trustworthy AI assessment list (ALTAI) and the proposed AI Act using an upper-level ontology for semantic interoperability between trustworthy AI documents with a focus on activities. The comparison is provided as an RDF resource graph to enable further enhancement and reuse in an extensible and interoperable manner.
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