MJ Bracha Soultions

1.Inclusivity. This means meaningful participation from stakeholders across all regions, particularly from developing countries, and from diverse technical and non-technical backgrounds, regardless of their current level of AI development, adoption, or production. Also including those most affected by its impacts. 2.Balanced approach to innovation and ethics. This includes ensuring that governance frameworks support technological progress while safeguarding human rights, promoting fairness, and addressing disparities between developed and developing contexts. 3.Recognize and address the intersection between AI and global development priorities. The growing energy demands associated with AI systems raise important questions about sustainability. At scale, AI adoption may place increasing pressure on energy infrastructure, with potential implications for achieving Sustainable Development Goal 7 (ensuring access to affordable, reliable, sustainable, and modern energy for all). A successful outcome would include a commitment to integrating sustainability considerations into AI governance frameworks, ensuring that technological advancement does not come at the expense of climate and development goals. Finally, the dialogue should contribute toward actionable pathways such as strengthening international cooperation, supporting capacity-building, and ensuring that governance frameworks are implementable across different national contexts.

• Safe, secure and trustworthy AI
• Interoperability of governance approaches
• Open-source software, open data and open AI models
• Protection and promotion of human rights

The protection and promotion of human rights should remain a central consideration in every innovation, policy, and framework shaping our global society. Artificial intelligence has introduced both significant opportunities and complex risks in this regard. While it has the potential to advance human well-being, it also challenges existing safeguards in ways that can have unintended and, at times, harmful consequences. In recent years, the rapid pace of AI development has been largely driven by innovation and competition. While this has accelerated technological progress, it has also highlighted the need to more deliberately prioritize safety, security, and the protection of human dignity alongside advancement. This underscores the importance of developing globally harmonized approaches to AI governance: frameworks that ensure innovation proceeds in a manner that is aligned with human rights principles and societal well-being. In addition, promoting open, inclusive, and collaborative ecosystems - such as open-source models, platforms, and communities - can play a key role in democratizing access to AI, enhancing transparency, and fostering shared responsibility. These approaches can help ensure that AI systems are developed and deployed in ways that serve humanity broadly, while minimizing the risk of harm or disruption to social and cultural contexts.

While the identified themes provide a strong foundation, there may be an opportunity to further strengthen the dialogue by explicitly addressing regulatory approaches and enforcement mechanisms, which are critical for translating principles into practice.

Governance gaps in the selected thematic areas are contributing to uneven outcomes across regions. In some contexts, limitations in transparency, accountability, and harmonized regulatory approaches create environments where AI can be used in ways that risk enabling political suppression or reinforcing anti-competitive practices. At the same time, unclear or fragmented governance frameworks may unintentionally stifle innovation - either by creating uncertainty for developers and investors, or by allowing dominant actors to shape the ecosystem in ways that limit fair participation. This is particularly challenging in regions with emerging AI ecosystems, where both overregulation and under-regulation can have disproportionate impacts. One of the most significant challenges is ensuring that AI governance is informed by adequate technical expertise. In some cases, governance structures may not fully reflect the level of technical understanding required to effectively oversee rapidly evolving AI systems, particularly in a global and cross-border context.

By facilitating the development of interoperable governance approaches that can be integrated into existing multilateral frameworks. One potential contribution would be to support the drafting of intergovernmental guidance or frameworks that can be embedded within established institutions such as the United Nations Security Council and the International Telecommunication Union, particularly by building on existing cybersecurity governance efforts. By leveraging these established platforms, the AI Dialogue can help avoid fragmentation, strengthen coordination, and promote consistency in how AI risks and opportunities are addressed across borders.

1. It can draw from cybersecurity cooperation mechanisms within the UN system, as well as ongoing work by the International Telecommunication Union to provide valuable models for international coordination, confidence-building measures, and capacity-building across diverse national contexts. 2. Data protection and privacy frameworks offer important lessons in establishing rights-based principles, while also highlighting challenges related to cross-border enforcement and uneven regulatory capacity. These experiences are directly relevant to AI governance. 3. Multi-stakeholder initiatives involving governments, the private sector, academia, and civil society have contributed to the development of ethical AI principles, technical standards, and best practices. These collaborations demonstrate the importance of inclusive governance approaches. By bringing together lessons from cybersecurity, data protection, and multi-stakeholder initiatives, the Dialogue can help develop more coherent, globally applicable AI governance frameworks.