We’re happy to share notes from Nicole Tschap, European Affairs Policy Manager, Research & Innovation at Fujitsu, on the discussions about AI Governance from the latest AI4People Summit.
The session, titled Advancing Ethical AI Through Governance And Global Standards, brought together experts to discuss key issues, including trust, accountability, inclusivity and shared language in developing AI standards.
The discussion featured a panel of experts, including Robert Madelin, Member Scientific Committee AI4People Institute, former Director General DG Connect, European Commission; Nooshin Amirifar, PhD, Team Leader & account manager Electrotechnology & ICT Standardization at CEN & CENELEC, Virginia Dignum, Professor of Computer Science at Umeå University; Touradj Ebrahimi, Professor at EPFL and convenor of ISO/IEC JTC 1/SC29/WG 1 on JPEG normalization; Ansgar Koene, Global AI Ethics and Regulatory Leader, EY; Lyse Langlois, Director General, International Observatory on the Societal Impacts of AI and Digital Technology (OBVIA); Albina Ovcearenco, Secretary to the Committee on Artificial Intelligence at the Council of Europe; Enrico Panai, Professor of AI and NLP in Decision Making at Università Cattolica del Sacro Cuore; Jeannie Marie Paterson, Professor of Law and the Director of the Centre for AI and Digital Ethics, University of Melbourne; and Toby Walsh FAA FTSE FRSN, Laureate Fellow & Scientia Professor of AI at the School of CSE, UNSW Sydney.
To watch this discussion please click here
Current standard-setting systems
On December 3rd , working group 6 of the AI4People Summit brought together various experts on international standardisation and governance. Robert Madelin, former Director-General of DG Connect at the European Commission, incited a panel discussion on the role of standardisation and (global) governance in the field of Artificial Intelligence.
“The central challenge for international AI standardisation is that existing global standard-setting systems are too slow, too narrow, and too fragmented to handle fast-moving, socio-technical, and globally diverse technology like AI. This required more inclusive, flexible, and coordinated processes that integrate ethics, human rights, and regional differences while preventing dominance by powerful actors.”
Artificial Intelligence has already penetrated most areas of everyday life, although it only gained public attention during the 2020 launch of ChatGPT. Since then, AI innovation has been on a rapid pace, moving faster than a typical life cycle of stable hardware production, and faster than the traditional linear standardisation timeline of research, development and standardisation spanning over 3 years. The unpredictability that goes along with the fast market-driven cycles pushes for continuous updates and parallel processes, calling for the standardisation process to be more parallel instead of linear. Standards now must evolve dynamically alongside AI innovation to remain relevant. Mr Ebrahimi argues that the systems must thus become more agile by introducing sandbox-style standardisation processes, allowing them to be iterative and flexible as innovation changes the landscape.
Socio-Technical and Ethical Changes in AI Standardisation
As the process not only becomes market-driven but also inter-sectoral, meaning AI being part of nontraditional IT sectors such as healthcare or justice, standardisation requires expertise that goes beyond engineering. To successfully develop standards for all sectors, also including ethical and social guidelines, lawyers, researchers, human rights activists, labour representatives, as well as civil society must be invited to the table, painting a more cohesive picture of the actual innovation deployment. However, two main aspects are limiting the success of diverse approaches: 1) lack of funding and 2) lack of a common language. While the former limits the participation of smaller actors and thus increases the risk that big tech dominates the processes without safeguards, the latter requires more effort than just monetary resources. As different stakeholders need to not only agree on ethical norms that are highly contextual and cultural, but also find common grounds to balance globality and the specificity of norms, the governance of the standardisation process is increasingly important.
“AI is not only an object of standardisation but also a force that reshapes the entire standardisation process, requiring faster, parallel, and more ethically grounded standards that address AI-specific risks (like deepfakes, opacity, and probabilistic behaviour) while ensuring trustworthy, equitable, and globally coherent deployment.”
Compared to traditional technology, modern innovation strikes not only in its rapidity but also in its nature of being the recipient as well as the influencer of the very standardisation processes. As the aforementioned traditional timeline is no longer viable, and the stakeholder groups need to be more diverse, standards must now display a socio-technical and ethical complexity, which makes trust its most important factor, according to Ms Paterson. Standards alone are seemingly not sufficient anymore to convey this trust, being why human capital needs to be well integrated into international standards in order to solidify trust in fast-paced AI innovation. AI forces standardisation to embed ethics and governance into its processes, making trustworthiness, accountability, responsibility and social values integral parts of its development. Accountability is especially highlighted by Mr Panai, while Mr Walsh underlines the opacity and concentration of AI development as a major source for lack of trust. Thus, ethical infrastructures and regulatory goals such as risk management and governance are cornerstones to achieve balance and shape real-world compliance.
Cross-Border Cooperation
However, not only the sectoral but also the global fragmentation and jurisdictional diversity are challenging the future of standardisation processes. While different national and supranational approaches stemming from the EU, US, or the Asia-Pacific region serve a culturally sensitive approach to standardisation, they also risk global misalignment. The aforementioned opacity of new AI models, paired with fragmented legislation, leads to complicated interoperability. Standards serve as a bridge between jurisdictions, supporting the impact assessments and thus enabling cross-border cooperation. To alleviate the ramification of global AI risks, Mr Walsh highlights the importance of open-source models and smaller, specialised AI.
“Global governance of AI increasingly relies on international standards as the practical mechanisms that translate ethical principles and policy goals into interoperable, cross-border, and trustworthy implementation frameworks—requiring inclusive participation, shared terminology, and ethical foundations that respect cultural diversity amid geopolitical competition and rapid technological change.”
Lastly, standards are essential tools for operationalising global AI governance as they translate high level policy and regulatory goals into concrete, implementable practices. Thus, it comes as no surprise that the standardisation community is actively working to alleviate the future challenges identified by all of the speakers. Standards are now supporting policymakers and creating shared vocabularies for international cooperation, creating the aforementioned shared language. Additionally, they now must be part of an “ethical infrastructure” that shapes responsible global AI use while avoiding a one-size-fits-all ethical approach, honing in on cultural differences. An example of this changed approach is the Seoul Declaration of May 2024, highlighting the need to embed ethics, rights, accountability and transparency into the standardisation process.
