Connor Consulting Corp

A successful first Global Dialogue on AI Governance would not be measured only by declarations or high-level principles, but by its ability to genuinely integrate perspectives from different sectors, regions, and social realities into a shared and actionable global conversation. One of the most important outcomes would be creating a trusted environment where governments, private sector leaders, academia, civil society, and technical communities can openly discuss both the opportunities and risks of AI without reducing the discussion to purely geopolitical or commercial interests. AI governance cannot be shaped exclusively by governments or technology companies because its impact affects society as a whole. The roadmap itself already demonstrates an important effort toward inclusiveness through consultations across different countries, virtual participation, stakeholder engagement, and open calls for submissions. In my view, success would mean that these consultations are not treated as symbolic exercises, but that diverse contributions meaningfully influence the themes, priorities, and future governance approaches emerging from the Dialogue. Another important outcome would be building greater global alignment around core principles such as transparency, human oversight, accountability, dignity, non-discrimination, and protection of vulnerable groups, while still respecting cultural and regional differences. The Dialogue should also help bridge the growing gap between the speed of AI adoption and society's understanding of its ethical, legal, and social implications. Finally, I believe success would mean establishing an ongoing and adaptive governance process rather than a one-time event. AI evolves too quickly for static regulation alone. The Global Dialogue has the opportunity to become a continuous collaborative mechanism capable of evolving alongside technological development while preserving human values, trust, innovation, and international cooperation in a balanced and responsible way.

• Safe, secure and trustworthy AI
• Social, economic, ethical, cultural, linguistic and technical implications of AI
• Transparency, accountability, and human oversight
• AI capacity-building

My priorities for urgent action and active engagement are: Safe, secure and trustworthy AI; Social, economic, ethical, cultural, linguistic and technical implications of AI; Protection and promotion of human rights; and Transparency, accountability, and human oversight. These priorities are deeply interconnected and reflect the growing need for AI governance frameworks that remain human centered while addressing the speed and scale of technological adoption across societies and industries. Safe, secure and trustworthy AI is essential because AI systems are increasingly integrated into critical areas such as healthcare, education, cybersecurity, public services, professional environments, and decision-making processes. Trust cannot exist without security, reliability, appropriate safeguards, and clear accountability mechanisms capable of reducing risks related to misuse, manipulation, discrimination, hallucinations, data exposure, and unintended harm. I also consider the social, ethical, cultural, linguistic, and technical implications of AI a priority because AI is already reshaping labor dynamics, creativity, communication, education, and human interaction itself. The global discussion should not focus only on technical performance and innovation, but also on the societal and psychological consequences of AI adoption, including concerns related to dignity, identity, bias, exclusion, misinformation, and the growing difficulty of distinguishing human generated from synthetic interaction and content. The protection and promotion of human rights must remain central to any governance approach. As AI systems become more influential in professional, governmental, and social contexts, it is essential to preserve privacy, non-discrimination, freedom of expression, due process, and protections for vulnerable groups. Finally, transparency, accountability, and meaningful human oversight are fundamental for building public trust. AI systems should not make human responsibility invisible. People should be able to understand when AI is being used, challenge outcomes when necessary, and ensure that final accountability and ethical judgment remain meaningfully human.

The current global discussion on AI governance often focuses on how humans use AI systems, but less attention is given to how AI systems themselves are increasingly designed to imitate human interaction, communication, and even creativity. As some developers aim to make AI language and behavior as human-like as possible, we may need clearer ethical boundaries regarding transparency and the distinction between human and AI generated interaction. This discussion goes beyond technical governance and touches dignity, identity, trust, and social perception. Today, many employees feel uncomfortable disclosing AI assisted work because they fear being perceived as less capable or less creative. This creates a paradox where organizations encourage AI adoption while workplace culture may indirectly discourage transparency. Studies and social behavior trends also suggest that this pressure may disproportionately affect vulnerable groups, including women, who may face greater reputational concerns when acknowledging AI use. At the same time, society increasingly struggles to distinguish authentic human creation from synthetic generation. Virtual influencers, AI generated personalities, synthetic voices, and emotionally humanized assistants demonstrate how blurred this boundary is becoming. Transparency therefore becomes not only a compliance principle, but also a social and ethical safeguard. Human centered AI governance should preserve the visibility of human judgment, accountability, and creativity instead of making them invisible behind automated systems. The objective should not be to discourage innovation, but to ensure that technological evolution does not erode human authenticity, trust, or dignity. Governance frameworks may therefore need to evolve beyond regulating only how humans use AI systems. They may also need to address how AI systems are intentionally designed to simulate human behavior and what ethical limits or disclosure obligations should exist in these scenarios. The challenge is to foster innovation and technological advancement without losing what makes human interaction and responsibility fundamentally human.

In my perspective, one of the main governance gaps affecting the professional and technology sectors is the growing misperception that AI innovation and automation are completely dissociated from human intervention, judgment, and capability. Public narratives increasingly present AI as a replacement for human work instead of recognizing that, in most real scenarios, AI systems still depend heavily on human supervision, validation, creativity, and decision making. This perception is already generating important social and economic impacts. Large scale layoffs in the technology sector are frequently associated with AI driven automation strategies, reinforcing fear, uncertainty, and resistance among workers. At the same time, organizations strongly promote AI adoption as a business strategy while workplace culture often discourages transparency regarding its actual use. One of the most significant governance gaps is the absence of frameworks that normalize transparent and responsible AI assisted work while preserving human recognition and accountability. Many employees still feel uncomfortable acknowledging AI use because they fear being perceived as less capable, less creative, or professionally replaceable. This concern may disproportionately affect minority groups, including women, due to existing social and professional biases. As a result, organizations face a contradiction where AI adoption is heavily encouraged at the strategic level while actual adoption is partially blocked by fear, stigma, and lack of psychological safety among employees. This creates risks not only for inclusion and workforce trust, but also for governance itself, since hidden AI usage reduces transparency and accountability. At the same time, there is a major opportunity to build governance models that position AI as a tool that augments human capability rather than replacing human value. Transparent AI practices, meaningful human oversight, ethical disclosure standards, and inclusive workplace cultures can help foster innovation while preserving dignity, trust, creativity, and human centered accountability.

The AI Dialogue can play a critical role in advancing international cooperation by creating a continuous and inclusive space where governments, private sector leaders, academia, civil society, and technical communities can collectively discuss not only the opportunities of AI, but also its social, ethical, economic, and human implications. One of the main challenges in AI governance today is that technological development evolves globally while regulation, social adaptation, and governance approaches remain fragmented. Different countries and sectors are advancing at different speeds, often with different priorities, legal systems, and cultural perspectives. The AI Dialogue has the opportunity to reduce this fragmentation by encouraging cooperation around common principles such as transparency, accountability, human oversight, protection of human rights, and trustworthy AI. The Dialogue can also help shift the global discussion beyond purely technical or geopolitical competition. AI governance should not focus only on innovation and automation, but also on how these transformations affect labor dynamics, social trust, inclusion, education, creativity, and human dignity. International cooperation is essential because the societal effects of AI are cross border and increasingly interconnected. Another important role of the AI Dialogue is to amplify perspectives that are often underrepresented in global technology discussions, including voices from the Global South, minority groups, workers, and sectors directly affected by rapid AI adoption. This is particularly important in discussions around transparency and the social pressure surrounding AI assisted work, where many individuals still fear being perceived as less capable if they openly acknowledge AI use. Finally, the AI Dialogue can help establish governance as an adaptive and collaborative process rather than a static regulatory exercise. AI evolves too quickly for isolated national approaches alone. International cooperation will be essential to preserve trust, inclusion, innovation, and meaningful human accountability in an increasingly AI driven world.

The AI Dialogue should build upon existing international initiatives and governance frameworks that already provide important ethical, legal, and human centered foundations for AI governance. These include the UNESCO Recommendation on the Ethics of Artificial Intelligence, the OECD AI Principles, the European Union AI Act, the United Nations discussions on trustworthy AI, and national governance initiatives emerging across different regions. These frameworks already contribute valuable principles related to transparency, accountability, human oversight, human rights, non-discrimination, safety, and trustworthy AI. At the same time, the AI Dialogue can bring important added value by connecting perspectives that are often discussed separately. One of its greatest strengths is the ability to concentrate contributions from governments, private sector leaders, academia, technical communities, civil society, and populations directly affected by AI adoption. This multi stakeholder structure creates the opportunity for a more balanced and globally representative discussion. In this context, the Dialogue could help advance governance discussions beyond regulating only how humans use AI systems. Governance frameworks may also need to evolve to address how AI systems themselves are intentionally designed to simulate human behavior, emotional interaction, communication, and even creativity. As AI becomes increasingly human like in language and interaction, clearer ethical limits and disclosure obligations may become necessary to preserve transparency, trust, and authentic human interaction. The AI Dialogue could therefore play an important role in identifying emerging governance gaps that are not yet fully addressed by existing regulations, particularly the social and psychological implications of increasingly humanized AI systems. It can also help promote international alignment on ethical disclosure standards, transparency in AI generated interaction and content, and the preservation of meaningful human accountability and dignity in increasingly AI mediated environments. Finally, the Dialogue can create a continuous collaborative mechanism capable of adapting governance discussions as technology evolves, rather than relying only on static regulatory approaches.

The current format of the AI Dialogue already represents an important step toward inclusive participation because it combines consultations with Member States, stakeholder engagement, regional discussions, virtual participation, open submissions, and in person meetings. I believe maintaining this multi stakeholder structure is essential for the legitimacy and effectiveness of the Dialogue. AI governance affects governments, private sector organizations, academia, civil society, workers, educators, technical communities, and the general public. For this reason, different stakeholders should continue contributing through open consultations, thematic working groups, public submissions, regional forums, technical panels, and interdisciplinary discussions that allow both technical and non-technical perspectives to be heard. One important recommendation is to ensure that participation is not limited to large governments or major technology companies. The Dialogue should continue creating space for voices from the Global South, smaller organizations, vulnerable groups, independent researchers, and professionals directly affected by AI adoption in everyday work environments. Many societal impacts of AI, particularly related to labor dynamics, transparency, education, inclusion, and psychological safety, are often better identified by those experiencing these transformations directly. I also believe the combination of virtual and in person participation should remain a priority because it increases accessibility and global representation. Open calls for submissions are particularly valuable because they allow broader societal contributions beyond institutional participation. From a structural perspective, maintaining thematic areas while encouraging cross thematic discussions could also be beneficial, since many AI governance challenges are interconnected. Topics such as transparency, human rights, labor impacts, trust, and human oversight cannot always be addressed in isolation. Finally, I believe the Dialogue should continue operating as an ongoing and adaptive process rather than a one-time event. Continuous engagement and periodic reassessment will be essential because AI technologies and their societal impacts evolve very rapidly.

One of the most underrepresented perspectives in global discussions on AI governance is the Global South. Although AI governance is often discussed as a global challenge, many debates are still heavily influenced by the priorities, economic realities, and regulatory perspectives of more technologically advanced countries and large technology companies. For many regions in the Global South, the priorities surrounding AI are often fundamentally different. Discussions about advanced automation, frontier models, or highly sophisticated governance frameworks may not always reflect local realities where basic challenges such as digital infrastructure, access to education, economic inequality, labor vulnerability, cybersecurity capacity, and technological dependence remain central concerns. This creates an important imbalance. AI governance discussions risk becoming disconnected from the realities of populations that may experience the social and economic impacts of AI adoption most intensely while having less influence over how these technologies are developed and regulated globally. At the same time, achieving true balance will not be simple because priorities are not uniform across regions. Some countries focus primarily on innovation competitiveness and frontier AI risks, while others are more concerned with economic inclusion, workforce impact, access to technology, data governance, and digital sovereignty. These differences should not be treated as obstacles, but as evidence that AI governance cannot rely on a one size fits all approach. To improve inclusion, the AI Dialogue should continue strengthening regional consultations, multilingual participation, virtual accessibility, and open submission mechanisms. It is also important to include not only governments and large organizations, but also educators, workers, minority groups, smaller enterprises, independent researchers, and civil society representatives directly affected by AI adoption. Finally, inclusion should not mean only participation in discussions, but meaningful influence in shaping governance priorities, principles, and future frameworks. A truly global AI governance model must reflect diverse realities, capacities, and societal needs.

One innovative engagement format that could strengthen the AI Dialogue would be the inclusion of structured global surveys and practical assessments across real work environments and social sectors. These surveys could help identify the gap between AI adoption as a strategic priority and the reality of how AI is actually being used, perceived, and disclosed by people in their daily professional and social environments. Today, many organizations strongly promote AI adoption as part of innovation strategies, while employees and institutions may still face uncertainty, fear, lack of training, or cultural resistance regarding its actual use. Gathering practical insights from workplaces, educational institutions, public services, healthcare, academia, and technical sectors could provide a more realistic understanding of current governance challenges, workforce concerns, transparency issues, and barriers to responsible adoption. I believe these assessments should include not only the private sector, but also governments, universities, schools, civil society organizations, and vulnerable or underrepresented groups. This would allow the Dialogue to better understand how AI impacts different realities, professions, cultures, and socioeconomic contexts. Regarding the Global South specifically, it would also be important to include experts from universities, research centers, and both public and private sectors who can highlight the real operational, economic, educational, and infrastructure challenges surrounding AI adoption. In many cases, global governance discussions focus heavily on frontier AI risks and advanced technological capabilities while regions in the Global South may still be dealing with more basic concerns such as digital access, workforce adaptation, education, cybersecurity capacity, technological dependence, and inequality. Another valuable format could be interdisciplinary roundtables combining technical experts with professionals from law, education, labor, psychology, ethics, and social sciences. AI governance challenges are not purely technical and require broader human centered discussions. Finally, maintaining virtual participation and open public submissions remains essential to ensure accessibility, diversity of perspectives, and continuous global engagement beyond formal institutional participation.

Some examples of practices and approaches that can promote effective AI governance are risk-based governance models, AI ethics committees, transparency obligations, human oversight mechanisms, AI literacy programs, and algorithmic impact assessments adapted to each business model and sector. One important approach is the adoption of governance frameworks that classify AI systems according to risk levels and apply proportional controls depending on the sensitivity of the activity involved. AI systems interacting with sensitive operational, technical, financial, educational, healthcare, or third-party data should require stronger safeguards, including human oversight, documentation, traceability, cybersecurity protections, validation procedures, and continuous monitoring. I also believe governance should not rely only on static regulation. Different sectors face different realities and vulnerabilities. Healthcare, education, cybersecurity, finance, labor environments, and public administration each present distinct operational, ethical, and social risks. For this reason, governance should include continuous risk mapping and algorithmic impact assessments capable of evaluating transparency, discrimination risks, cybersecurity exposure, data protection, human rights impacts, and workforce implications according to each specific activity and business model. This approach is strongly aligned with risk-based governance principles already reflected in international initiatives such as UNESCO, OECD, and the EU AI Act. Another important practice is maintaining meaningful human oversight. AI systems should support decision making rather than make human responsibility invisible. Transparency regarding AI assisted outputs, explainability, auditability, and clear accountability structures are essential for trust. Finally, AI literacy and workforce enablement are critical. Rapid AI adoption without adequate education and governance increases risks related to misinformation, hidden AI usage, cybersecurity vulnerabilities, bias, and lack of accountability. Effective governance therefore requires not only technical controls, but also continuous human training, ethical awareness, and inclusive organizational culture.