Minnesota Responsible AI Institute

A successful first Global Dialogue on AI Governance would reflect what we've already learned from the adoption of AI governance practices around the world and use that as a foundation to move the conversation forward. We now have meaningful evidence of how AI is shaping businesses, communities, schools, and everyday life. We also know that engaging diverse stakeholders is not optional, it's essential. While we may not yet fully understand all of AI's benefits or long-term impacts, we do have enough insight to better understand how it is affecting employees, citizens, and communities and how people are responding to it as both a disruptor and an accelerator. At the Minnesota Responsible AI Institute, we believe success would mean expanding the dialogue to include the earlier, more human questions: Why are we choosing this technology, for this community, this organization, this learning environment? Who does it serve? And what does it ask of the people living and working alongside it? We would also hope to see continued broadening of participation, bringing in voices beyond technical and legal expertise, especially those experiencing these shifts in real time. Finally, success should be something people can recognize and explain in their own lives, not something distant or abstract, but something that allows them to say: I see myself in this conversation and effort, and I know it's being shaped with me in mind.

• Social, economic, ethical, cultural, linguistic and technical implications of AI
• Protection and promotion of human rights

Our selections reflect both what we are seeing in practice and where we believe the global conversation needs to go next. Living and working in Minneapolis, Minnesota, (USA) we also see how perspectives on AI are shaped by local context, including political climate and community experience. People are not only responding to AI through their lived realities, what they trust, what they question, and what they feel is at stake. This makes the protection and promotion of human rights especially important, as these systems intersect with real concerns about fairness, access, and representation. Ultimately, our priorities reflect a belief that AI governance must move beyond technical and compliance-focused approaches to include the human and political realities shaping use adoption, ensuring governance is informed not only by expertise, but by those living through these changes in real time.

One cross-cutting issue that is not fully captured in the themes above is how AI is being experienced by people navigating essential aspects of daily life, health, education, food, and housing, within contexts of social tension and, at times, civil unrest. Living and working in Minneapolis, Minnesota, we see firsthand how communities are already carrying layers of complexity, economic pressure, shifting public trust, and differing lived experiences shaped by race, geography, and access. AI is entering into these conditions, not apart from them. What is experienced here is not unique. Similar dynamics are unfolding in cities and regions around the world, where individuals and communities must make sense of and respond to AI while also navigating broader social, political, and economic pressures. In these environments, the ability to question, influence, or even understand how AI is being used is not evenly distributed. This creates a critical governance gap. More discussion is needed to center how people are able to engage with AI and Life especially when trust is fragile or conditions are unstable. AI governance must expand to include not just systems and structures, but the conditions in which people are being asked to live alongside them.

Not everyone has the same ability to question decisions, advocate for themselves, or even recognize when AI is shaping outcomes. In places like Minneapolis, and in communities around the world, people are navigating AI while also managing broader social, economic, and political pressures. This creates uneven conditions for understanding, responding to, or influencing how AI is used. This gap presents both a challenge and an opportunity. The challenge is that governance risks becoming something done to people, rather than with them, particularly in communities where trust may already be fragile. The opportunity is to design governance approaches that are more participatory, context-aware, and grounded in lived experience. This includes expanding who is involved, how decisions are communicated, and how people are supported in navigating AI in real time. Ultimately, effective governance will require a deeper connection to the human conditions in which those systems operate.

A global survey, real voices or anonymized voices.

At the Minnesota Responsible AI Institute, we would welcome the opportunity to host global leaders in Minneapolis, to learn directly from community members navigating both the challenges and opportunities of AI in real time. We need the Global Dialogue on AI Governance to come closer to people. To move beyond conference rooms and into the places where AI is actually being lived, questioned, and experienced. We are well positioned to host a week-long immersive experience that brings leaders into community, across education, workforce, and local systems so they can see, listen, and engage with the realities shaping AI adoption on the ground.

Same answer as 14.

Same as 14.

Same as 14.

One approach I would highlight is what I've come to understand the "Question Zero" effort observed in practice at the AI Policy Lab at Umeå University in Sweden. At its core, the work invites a pause before action. Before implementing or deploying AI, individuals and teams are encouraged to ask a foundational question: Should we be doing this at all? Effective AI governance will require not only formal policies and frameworks, but also shared practices like this, approaches that people can use in real time, in context, as they navigate decisions. Sometimes the most impactful governance begins not with more answers, but with better questions. Questions that combine various methods of getting answers.