New York University

As a researcher working at the intersection of robotics, artificial intelligence, and human-centered engineering, I believe the first Global Dialogue on AI Governance would be successful if it moves beyond broad principles and produces actionable, internationally collaborative frameworks that can guide the safe and responsible deployment of AI systems in the real world. In fields such as medical robotics, autonomous systems, and AI-assisted decision-making, the challenge is no longer whether AI will be integrated into society, but how we ensure that these systems remain transparent, safe, equitable, and aligned with human values. A successful dialogue should establish shared governance priorities across nations, academia, industry, healthcare institutions, and emerging quantum computing communities. From my perspective in robotics and embodied AI, governance discussions must address not only software-based AI models, but also physical AI systems that directly interact with humans through sensing, motion, autonomy, and decision-making. This includes standards for safety validation, accountability, data integrity, cybersecurity, human oversight, and real-time monitoring of autonomous behavior. An equally important topic is the rise of quantum computing as a potential computational foundation for next-generation AI. Quantum technologies may dramatically accelerate optimization, simulation, data processing, and large-scale reasoning tasks that are currently computationally expensive for classical systems. As quantum-enhanced AI becomes increasingly feasible, the global community must proactively discuss governance mechanisms surrounding computational access, security risks, encryption challenges, energy demands, and equitable distribution of advanced AI capabilities. Without international coordination, the combination of AI and quantum computing could widen technological inequalities and create new geopolitical and ethical concerns. Another important outcome would be the establishment of open international collaborations and regulatory sandboxes that allow researchers, startups, and policymakers to responsibly test emerging AI and quantum-enabled systems while still encouraging innovation. Governance should not become a barrier that only large organizations can navigate. Instead, it should support interdisciplinary education, scientific transparency, and inclusive participation from both developed and developing nations. Finally, success would mean creating a long-term adaptive governance mechanism capable of evolving alongside rapid advances in embodied AI, robotics, large language models, and quantum computing, ensuring that technological progress continues to benefit society while minimizing global risks.

• Safe, secure and trustworthy AI
• AI capacity-building
• Transparency, accountability, and human oversight
• Open-source software, open data and open AI models

My priorities for urgent action and active engagement focus on ensuring that AI development remains safe, human-centered, globally accessible, and socially responsible. As a researcher working in robotics, embodied AI, and intelligent medical systems, I believe the most important thematic areas are closely connected to the real-world deployment of AI technologies that directly interact with people. First, Safe, secure and trustworthy AI is essential because AI systems are increasingly integrated into healthcare, robotics, autonomous systems, and critical infrastructure. In these environments, failures in perception, reasoning, or control can have significant physical and societal consequences. International collaboration is needed to establish robust standards for safety validation, cybersecurity, reliability, and risk management for both software-based AI and physical AI systems. Second, Transparency, accountability, and human oversight are critical as AI systems become more autonomous and influential in decision-making. Humans must remain capable of understanding, supervising, and intervening in AI-driven processes. This is especially important in areas such as medical robotics, large language models, and future quantum-enhanced AI systems, where decision pathways may become increasingly complex. Third, AI capacity-building is important to ensure that the benefits of AI are shared globally. Expanding education, technical training, and interdisciplinary collaboration can help reduce inequalities in access to AI technologies and empower the next generation of researchers, engineers, and innovators. Responsible AI governance should support innovation while enabling broader participation across countries and communities. Finally, I prioritize the social, economic, ethical, cultural, linguistic and technical implications of AI because AI is reshaping society far beyond the technical domain. Emerging technologies, including embodied AI and quantum computing as the computational foundation for next-generation AI, will influence healthcare, labor, communication, education, and global equity. Governance discussions must therefore address not only technical performance, but also long-term societal impact and human values.

Yes. While the listed themes provide a strong foundation, several emerging and cross-cutting issues deserve greater attention due to the rapid evolution of AI and embodied intelligent systems. One important area is the convergence of AI with robotics and physical systems. Much of today's governance discussion focuses on software-based AI models, yet embodied AI systems such as humanoid robots, autonomous surgical robots, drones, and industrial robotic platforms directly interact with humans and physical environments. These systems introduce additional concerns related to real-time safety, mechanical reliability, sensor integrity, physical autonomy, and human-robot interaction that require dedicated governance frameworks beyond conventional digital AI regulation. Another emerging issue is the integration of quantum computing with next-generation AI. Quantum-enhanced AI could significantly accelerate optimization, simulation, and large-scale reasoning capabilities, potentially reshaping cybersecurity, scientific discovery, autonomous systems, and global computational power distribution. Governance discussions should proactively address equitable access, computational concentration, encryption vulnerabilities, and the geopolitical implications of quantum-enabled AI before these technologies mature at scale. AI sustainability and infrastructure impact also deserve more attention. Training and deploying large-scale AI systems require significant energy, computational resources, rare materials, and data-center infrastructure. Future governance frameworks should consider environmental sustainability, energy efficiency, and responsible resource allocation alongside technical advancement. In addition, educational transformation and workforce adaptation are critical cross-cutting issues. AI is fundamentally changing how students learn, how engineers are trained, and how future workforces will interact with intelligent systems. Governance should support AI literacy, interdisciplinary education, and human-centered skill development rather than focusing solely on regulation and risk mitigation. International scientific interoperability remains an important challenge. Differences in regulatory standards, datasets, safety benchmarks, and evaluation methodologies across countries may limit collaboration and slow responsible innovation. Establishing globally shared technical standards and validation frameworks could help support safer and more equitable AI development worldwide.

In the robotics, healthcare, and engineering sectors, the rapid advancement of AI is significantly outpacing existing governance frameworks. One major challenge is the lack of standardized regulations and validation methods for AI systems that directly interact with humans, particularly embodied AI systems such as medical robots, rehabilitation devices, and autonomous robotic platforms. In the United States and globally, many organizations are actively developing AI-enabled systems, yet there remains uncertainty regarding accountability, safety certification, data governance, cybersecurity, and human oversight. This regulatory fragmentation creates barriers for responsible deployment and international collaboration. In healthcare robotics specifically, governance gaps affect the safe integration of AI-assisted surgical systems, clinical decision-support tools, and autonomous robotic devices. Questions surrounding liability, explainability, and validation remain unresolved, especially when AI systems make adaptive or partially autonomous decisions. Similarly, the growing use of large language models in education and engineering introduces concerns regarding misinformation, bias, intellectual ownership, and the long-term impact on human critical thinking and workforce development. At the same time, these advances also create significant opportunities. AI has the potential to improve healthcare accessibility, accelerate scientific discovery, enhance manufacturing productivity, and support more personalized education and rehabilitation systems. In robotics, AI-driven perception, control, and simulation are enabling safer and more intelligent human–machine collaboration across industries. An emerging opportunity and challenge is the integration of quantum computing with next-generation AI systems. Quantum-enhanced AI could dramatically increase computational capabilities for optimization, simulation, and autonomous reasoning. However, it may also widen technological inequalities between countries and organizations with unequal access to advanced computational infrastructure. From my perspective, the most important opportunity is establishing internationally coordinated governance frameworks that encourage innovation while ensuring safety, transparency, accountability, and equitable access. Strong global collaboration between academia, industry, governments, and research institutions will be essential to ensure that AI development benefits society responsibly across sectors and regions.

The AI Dialogue can play a critical role in advancing international cooperation on AI governance by creating a neutral and collaborative platform where governments, researchers, industry leaders, educators, and civil society can work together to address the rapid evolution of AI technologies. As AI systems continue to expand across robotics, healthcare, education, manufacturing, and public infrastructure, governance efforts remain fragmented across countries and sectors. The Dialogue can help establish shared principles, interoperable standards, and coordinated strategies for safety, transparency, accountability, cybersecurity, and human oversight. One particularly important role of the AI Dialogue is supporting open scientific collaboration and responsible open-source innovation. Open-source AI models, datasets, simulation platforms, and robotics frameworks have significantly accelerated research, education, and technological accessibility worldwide. In robotics and embodied AI research, open-source ecosystems such as ROS, Gazebo, Isaac Sim, and publicly shared machine learning models have enabled researchers, startups, and students to contribute to innovation regardless of institutional size or geographic location. International cooperation should therefore encourage responsible open-source development while also addressing associated risks related to misuse, security vulnerabilities, data governance, and model reliability. The Dialogue can also help the global community proactively address emerging technologies, including embodied AI systems and the future convergence of AI with quantum computing. Quantum-enhanced AI may dramatically reshape optimization, simulation, scientific discovery, and autonomous reasoning capabilities, creating both opportunities and geopolitical challenges. International collaboration will be essential to ensure equitable access and responsible governance of these advanced computational technologies. In addition, the AI Dialogue can strengthen AI capacity-building by promoting educational partnerships, interdisciplinary training, and inclusive participation from developing countries and underrepresented communities. Ultimately, the Dialogue can help establish adaptive and evidence-based governance frameworks that evolve alongside AI advances while ensuring that innovation remains open, safe, trustworthy, and beneficial to society globally.

The AI Dialogue should build upon existing international initiatives, open scientific communities, academic–industry partnerships, and standards organizations that are already shaping responsible AI development across sectors. Important examples include the work of the United Nations Educational, Scientific and Cultural Organization on AI ethics, the Organisation for Economic Co-operation and Development AI Principles, the International Organization for Standardization standards initiatives, the Institute of Electrical and Electronics Engineers efforts on ethically aligned design, and international collaborations led by universities, healthcare institutions, and open-source research communities. These initiatives have already contributed important foundations for AI ethics, transparency, interoperability, and technical safety standards. The Dialogue should also connect with open-source ecosystems and collaborative research platforms that accelerate innovation and accessibility. In robotics and embodied AI research, frameworks such as Robot Operating System, Gazebo, and NVIDIA Isaac Sim have enabled global collaboration among researchers, startups, educators, and students. Similarly, open-source AI communities have played a major role in democratizing access to machine learning tools, datasets, and computational frameworks. The AI Dialogue can help promote responsible open innovation while coordinating international approaches to safety, cybersecurity, model validation, and governance. An important added value of the AI Dialogue would be its ability to unify currently fragmented efforts across governments, academia, industry, and civil society into a more globally coordinated framework. Many existing initiatives operate independently or focus on narrow technical or regional priorities. The Dialogue can create stronger interoperability between governance approaches while also addressing emerging areas that remain underrepresented, including embodied AI, medical robotics, AI for education, and the future convergence of AI with quantum computing. Most importantly, the AI Dialogue can serve as a long-term international platform that supports continuous collaboration, evidence-based policymaking, inclusive participation, and adaptive governance as AI technologies continue to evolve rapidly worldwide.

The AI Dialogue can benefit from an interdisciplinary and collaborative structure that enables meaningful participation from governments, academia, industry, civil society, healthcare institutions, educators, and open-source technology communities. AI governance affects not only software systems, but also robotics, healthcare, education, manufacturing, cybersecurity, and future computational infrastructures. As a result, no single stakeholder group can effectively address these challenges alone. Governments can contribute by developing internationally interoperable regulatory frameworks and supporting public-interest research, while academic institutions can provide evidence-based analysis, independent validation methodologies, and long-term perspectives on societal impact. Industry leaders and startups can provide practical implementation experience, technical expertise, and insights into emerging technologies and deployment challenges. Civil society organizations and educators can help ensure that governance discussions remain human-centered, inclusive, and socially responsible. Open-source communities also play an important role in the AI ecosystem. Open-source AI models, robotics frameworks, datasets, and simulation platforms have become major drivers of innovation, accessibility, and global collaboration. Including these communities in governance discussions can help balance innovation with safety, transparency, and accountability while reducing excessive concentration of technological power. In terms of structure, the AI Dialogue can combine high-level policy discussions with technical working groups focused on specialized topics such as embodied AI, healthcare AI, quantum-enhanced AI, cybersecurity, open-source governance, education, and sustainability. These working groups can include both technical experts and policymakers to encourage practical and evidence-based recommendations. The Dialogue can also prioritize international accessibility through hybrid participation models, multilingual engagement, open technical reports, and publicly available outcomes. Continuous engagement mechanisms, rather than one-time meetings, will remain important because AI technologies evolve rapidly. Regular workshops, collaborative research initiatives, regulatory sandboxes, and open scientific exchanges can help maintain momentum and strengthen long-term international cooperation. Ultimately, the AI Dialogue can function not only as a policy forum, but also as a global collaborative ecosystem that supports responsible innovation, knowledge sharing, and adaptive governance for the future of AI.

Several important voices remain underrepresented in global discussions on AI governance, particularly those from developing countries, interdisciplinary research communities, educators, healthcare practitioners, students, open-source developers, and researchers working on embodied AI and robotics. Current discussions are often dominated by large technology companies and a limited number of countries with strong computational and financial resources, which can create governance frameworks that do not fully reflect global needs, regional realities, or emerging application domains. Students, educators, and workforce development communities also deserve stronger representation because AI is fundamentally transforming education, learning processes, and future employment structures. Their perspectives are important for understanding how AI influences critical thinking, accessibility, digital literacy, and long-term societal adaptation. In addition, developing countries and smaller research institutions often face barriers related to computational access, infrastructure, funding, and participation in international policymaking. Hybrid participation models, multilingual engagement, publicly accessible technical resources, travel support, and open collaborative platforms could help expand inclusion.

Meaningful engagement during the AI Dialogue can be strengthened through interactive, interdisciplinary, and technically grounded formats that encourage collaboration beyond traditional panel discussions. Because AI governance affects diverse sectors including robotics, healthcare, education, cybersecurity, manufacturing, and scientific research, engagement mechanisms that combine policy, technical expertise, and real-world demonstrations can create more productive and actionable discussions. One effective format would be thematic simulated working space where policymakers, researchers, engineers, industry leaders, educators, and civil society representatives collaboratively analyze realistic AI governance scenarios. These sessions could focus on topics such as autonomous robotics, medical AI systems, open-source AI governance, cybersecurity risks, or quantum-enhanced AI. Practical case studies and simulated governance challenges can help participants move beyond abstract policy discussions toward evidence-based problem solving. Technical demonstration forums could also provide valuable engagement opportunities. Live demonstrations of embodied AI systems, robotics platforms, simulation environments, digital twins, and human–AI collaboration tools can help non-technical stakeholders better understand both the opportunities and risks associated with emerging technologies. This becomes increasingly important as AI systems move from purely digital environments into physical interaction with humans and infrastructure. Another innovative format involves open collaborative innovation sessions inspired by open-source communities. Researchers, students, startups, and international participants could contribute ideas, technical proposals, safety benchmarks, or governance recommendations in real time through shared digital platforms. This approach encourages transparency, accessibility, and broader global participation. Hybrid and multilingual engagement mechanisms are also essential for inclusive participation. Virtual technical workshops, interactive online forums, AI policy hackathons, and cross-regional collaborative sessions can help include participants from developing countries, smaller institutions, and underrepresented communities who may face financial or geographic barriers.

Several existing policies, platforms, and collaborative approaches provide valuable examples for advancing effective AI governance across technical, social, and regulatory dimensions. One important example is the AI ethics framework developed by United Nations Educational, Scientific and Cultural Organization, which emphasizes human rights, transparency, accountability, and inclusive access to AI technologies. Similarly, the Organisation for Economic Co-operation and Development AI Principles have helped establish internationally recognized guidance for trustworthy and human-centered AI development. An important emerging example is the AI Alliance led by IBM and Meta, which promotes open, safe, and responsible AI through international collaboration among industry, academia, and research institutions. The initiative emphasizes open-source innovation, scientific transparency, safety benchmarking, and shared governance tools that support responsible AI development. The Alliance demonstrates how open scientific ecosystems can accelerate innovation while also encouraging accountability, interoperability, and broader global participation in AI governance discussions. In technical governance, open-source ecosystems offer strong examples of collaborative and transparent innovation. Platforms such as Robot Operating System, Gazebo, and NVIDIA Isaac Sim enable researchers, educators, startups, and industry teams worldwide to openly develop, test, and validate robotics and AI systems. These platforms encourage reproducibility, peer review, interoperability, and shared safety practices, which are important components of responsible AI governance. Another promising approach involves regulatory sandbox environments, where researchers, companies, and policymakers can evaluate emerging AI technologies under controlled conditions before large-scale deployment. This model is particularly valuable for high-impact applications such as medical robotics, autonomous systems, and AI-assisted healthcare technologies, where safety validation and human oversight remain critical.