In this paper, we examine the challenges of developing international standards for Trustworthy AI that aim both to be global applicable and to address the ethical questions key to building trust at a commercial and societal level. We begin by examining the validity of grounding standards that aim for international reach on human right agreements, and the need to accommodate variations in prioritization and tradeoffs in implementing rights in different societal and cultural settings. We then examine the major recent proposals from the OECD, the EU and the IEEE on ethical governance of Trustworthy AI systems in terms of their scope and use of normative language. From this analysis, we propose a preliminary minimal model for the functional roles relevant to Trustworthy AI as a framing for further standards development in this area. We also identify the different types of interoperability reference points that may exist between these functional roles and remark on the potential role they could play in future standardization. Finally we examine a current AI standardization effort under ISO/IEC JTC1 to consider how future Trustworthy AI standards may be able to build on existing standards in developing ethical guidelines and in particular on the ISO standard on Social Responsibility.We conclude by proposing some future directions for research and development of Trustworthy AI standards.

Worldwide, there are a multiplicity of parallel activities being undertaken in developing international standards, regulations and individual organisational policies related to AI and its trustworthiness characteristics. The current lack of mappings between these activities presents the danger of a highly fragmented global landscape emerging in AI trustworthiness. This could present society, government and industry with competing standards, regulations and organisational practices that will then serve to undermine rather than build trust in AI. This chapter presents a simple ontology that can be used for checking the consistency and overlap of concepts from different standards, regulations and policies. The concepts in this ontology are grounded in an overview of AI standardisation currently being undertaken in ISO/IEC JTC 1/SC 42 and identifies its project to define an AI management system standard (AIMS or ISO/IEC WD 42001) as the starting point for establishing conceptual mapping between different initiatives. We propose a minimal, high level ontology for the support of conceptual mapping between different documents and show in the first instance how this can help map out the overlaps and gaps between and among SC 42 standards currently under development.

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