Page 30 - AI Governance Day - From Principles to Implementation
P. 30

AI Governance Day - From Principles to Implementation



                      The United Nations System published on 2 May 2024 a  UN System White Paper in AI
                      Governance: An analysis of the UN system’s institutional models, functions, and existing
                      international normative frameworks applicable to AI governance.

                      Potential discussion questions

                      •    What are the learnings that can be drawn multilateral and national AI governance efforts?
                      •    Which AI standards and regulations need to be agreed upon at the international level?
                      •    Within each category, with which priority should the various items be tackled?
                      •    Which process should be used to determine the prioritization of the various items?
                      •    What role should the UN take in international AI governance efforts?
                      •    How should participation in international AI governance decision-making be structured?
                      •    How do we address cross-border risks of AI systems?
                      •    How can we safely share knowledge about what is working and what is not working well?
                      •    How can regulation adapt and keep pace with technological advancements?


                      4.3.3  Insights from the breakout sessions: theme 1

                      The landscape of AI governance is intricate and evolving, with various approaches and
                      development stages across countries and regions. Key themes include human-centric
                      development, leveraging existing frameworks, inclusion, global coordination, private sector
                      involvement, and balancing governance with regulation.

                      •    Human-centric AI development: AI development should prioritize human welfare and
                           societal betterment, focusing on ethical principles and social good.
                      •    Leveraging existing frameworks: using existing regulatory frameworks from industries
                           like automotive, pharmaceuticals, and cybersecurity can streamline AI governance.
                           International organizations like the UN can provide unified principles to prevent
                           fragmentation.
                      •    Inclusivity and capacity building: addressing biases in AI and ensuring inclusive models
                           are essential. Enhancing AI capabilities in underrepresented regions, particularly in the
                           Global South, involves improving data collection and usability.
                      •    Multilateral and coordinated efforts: global coordination is crucial to prevent big tech
                           companies from setting standards. A multilateral approach is more effective than regional
                           or national efforts alone. Reducing fragmentation and consolidating efforts within strong
                           institutions is vital.
                      •    National and local applicability: implementing international AI governance frameworks
                           locally is challenging due to different adoption levels and needs. Smaller countries need
                           support to build necessary institutions.
                      •    Private sector involvement: private companies, especially in healthcare, must incorporate
                           value-based AI governance in their operations. The private sector is key in ethical AI
                           development.
                      •    Governance versus regulation: governance encompasses broader objectives than
                           regulation.  Initiatives  by  UNESCO  and  ITU  on ethics  and  standards  are  important.
                           Regulations must adapt quickly to keep pace with AI innovations.
                      •    Addressing linguistic and cultural divides: global frameworks must be balanced with
                           local contextualization to ensure inclusive AI development. Resolving linguistic and
                           cultural divides is crucial.
                      •    Data governance: data governance is fundamental in AI governance to prevent digital
                           colonization. Ensuring quality and accessible data and establishing cross-sector standards
                           are necessary.






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