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.
The development of intelligent (e.g., AI-based) applications increasingly requires governance models and processes, as financial legal sanctions are more and more being associated with violation of policies. We propose an ontology representing the informed consent that was collected by an organization and argue how it can be used to assess a dataset prior its use in any type of data processing activities. We demonstrate the utility of our ontology using a particular scenario, where datasets are generated "just in time" for a particular purpose such as sending newsletters. This scenario shows how data processing activities can be managed to in such a way as to support compliance verification. This paper furthermore compares the contributions to related work and positions it into prior work concerned with the broader problem of prescribing and analyzing compliance.
An organisation using personal data should document its data governance processes to maintain and demonstrate compliance with the General Data Protection Regulation (GDPR). As processes evolve, their documentation should reflect these changes with an assessment showing ongoing compliance. Through this paper, we show how semantic representations of processes are useful towards maintaining ongoing GDPR compliance by using a test-driven approach that generates and checks constraints for adherence to GDPR requirements. We first check whether all required information has been documented, and then whether it is compliant. We prototype our testing approach using a real-world website's consent mechanism for GDPR compliance, and persist results towards generating documentation. We use previously-published ontologies to represent processes (GDPRov), consent (GConsent), and GDPR (GDPRtEXT), with SHACL used to test requirement constraints.
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