We’re pleased to share notes from Jacob Livingston Slosser, PhD, LLM, Director of Research at Sapien Institute and iCourts Global Fellow at the University of Copenhagen Faculty of Law, on the discussions from the latest AI4People Summit.
The session, titled Navigating a World Transformed by AI, brought together leading experts to explore the challenge of turning human-centered AI from rhetoric into practical application.
Speakers included Danielle Allen , James Bryant Conant University Professor at Harvard University and Director of the GETTING Plurality Research Network; Ziyaad Bhorat, Senior Advisor for AI Ecosystem Strategy at Mozilla; Frincy Clement, Head of North America and Board Director at Women in AI; Sennay Ghebreab, Professor of Socially Intelligent AI at the University of Amsterdam; Olaf J Groth, PhD, Professor at the Haas School of Business at University of California Berkeley; Pierre Lévy, Associate Professor at Université de Montréal; Aida Ponce Del Castillo, working in Law, Science & Technology Foresight at the European Trade Union Institute; Jacob Livingston Slosser, PhD, LLM, Director of Research at Sapien Institute and iCourts Global Fellow at the University of Copenhagen Faculty of Law; and Anthony Vetro, President and CEO of Mitsubishi Electric Research Laboratories.
To watch this discussion please watch here
Human-centered AI: Rhetoric vs Reality
The panel opened with the question of what “human-centered AI” actually means in practice. Sennay Ghebreab, who has spent 15 years trying to build AI for public good, offered a direct assessment. Despite the term’s ubiquity in policy documents and corporate statements, meaningful inclusion of marginalized or vulnerable populations remains rare. His own lab’s trajectory, which he characterised as moving from building AI “for people” to “with people” to eventually “by people,” represents an aspiration more than an achievement. Much of what passes for progress, he suggested, is “ethics washing.” Institutions engage with frameworks and consult with citizens in ways that are portrayed as meaningful but often are not. What is missing, in his view, is institutional humility, meaning the willingness of academia, industry, and government to genuinely listen rather than perform consultation.
Jacob Livingston Slosser pushed the point further by questioning whether user-focused interventions at human centricity are fit for purpose. He cited empirical evidence that most people, most of the time do not notice when their judgment is being shaped, and telling them they are interacting with AI does not change this. Interventions like disclosure, literacy training, and explainability while likely necessary, may not be sufficient. This gap between rhetoric and reality was a recurring theme.
Global Power Dynamics
The discussion moved to the structural conditions shaping AI development, including economic competition, market concentration, and the institutions that might govern them.
Olaf Groth framed the global landscape with the US and China as dominant powers and uncertainty about who occupies the third position. Europe, despite leading in research and possessing substantial talent, cannot scale commercially. Its market remains fragmented, with digital services facing what Groth characterized as a 40% quasi-tax when crossing internal borders. Smaller countries including the UAE, Saudi Arabia, Kazakhstan, Israel, Malaysia, and Finland are moving aggressively to compete while Europe debates revisions to an AI Act not yet fully enforced.
The revision of that Act, which Slosser characterized as an effort to water down already modest protections, reflects a deeper tension. Groth argued that the competition between the US and China is not merely economic but a “values race,” framed by key decision-makers as democracy against autocracy. The current US administration’s position, he suggested, is that winning the values race requires first winning the economic race. This creates pressure to treat ethics as an obstacle rather than a design requirement.
Yet Groth resisted the framing of ethics and economics as trade-offs. Companies that integrate ethical safeguards from the design phase avoid costly downstream problems such as stakeholder backlash, class action lawsuits, and regulatory penalties. Salesforce’s ethical design architects, who work alongside product developers from day one, exemplify this integration. Anthony Vetro, representing Mitsubishi Electric Research Laboratories, offered a similar analogy from sustainability. Companies once treated environmental considerations as costs to minimize, but have come to see sustainability as value-adding. Ethics could be positioned the same way, as what Vetro called a “trade-on” rather than a trade-off.
The problem is that this integration is not happening at scale. With approximately $4.6 trillion in AI investment seeking returns, companies are pursuing the low-hanging fruit by reducing labor costs through layoffs rather than creating new value. The narrative of “FTE reduction” has become a way of compromising ethics in the name of recovering investment.
Workers and the Risks of AI Automation
This is where labor enters the picture, not as an abstract category but as the concrete site where AI governance either works or fails. Aida Ponce Del Castillo noted that AI systems now handle recruitment, task allocation, monitoring, and termination decisions. Some of these systems have already attracted regulatory sanctions and penalties. But workplaces are also where countervailing power has emerged. The Writers Guild of America strike, which lasted nearly 200 days, resulted in a collective agreement giving scriptwriters authority over whether to use generative AI. A European banking sector agreement limited facial recognition and personality assessment of employees. University teachers, meanwhile, are being recorded by webcams that measure their facial expressions and rank them on perceived empathy toward students. This use of AI has not yet been collectively bargained.
Ponce Del Castillo raised a concern that went beyond specific workplace applications. AI systems are designed to extract knowledge. Collaborative robots learn skilled tasks from human workers with minimal instruction, which sounds like augmentation until one recognizes that the machine is capturing tacit knowledge, the accumulated craft expertise that distinguishes experienced practitioners. Once that knowledge is extracted, the worker becomes expendable. She cited Daron Acemoglu’s Nobel Prize-winning work on the argument that without fundamentally changing how AI systems are produced, workers will never benefit from them. No existing company policy or law prevents this extractive dynamic. Major companies have announced layoffs explicitly justified by digitalization investments.
Vetro’s distinction between autonomy and authority offered one way to think about limits. AI systems may operate autonomously on specific tasks without having authority to make final decisions, particularly in safety-critical or ethically sensitive areas. The “human in control” principle originated in aviation, where the context is well-defined and pilots remain in the loop. But operationalizing this for journalists, teachers, lawyers, or factory workers is a different problem, and no one on the panel claimed to have solved it.
The question of who decides what tasks AI should take and what humans should retain led to broader reflections on governance and democracy. Danielle Allen framed the challenge as one of governance capacity. Democratic institutions are straining globally, and that strain reflects how difficult it is to navigate rapid economic and social transformation, which is itself largely driven by technology. For democratic institutions to succeed, they need to renovate themselves for adaptability and resilience.
AI for Transparent Governance
Allen described democratic institutions as having an “institutional spine” with three vertebrae, namely citizen participation, decision-maker deliberation, and implementation. AI tools could strengthen each. Sense-making tools for citizen input, as used in Taiwan, help rebuild legitimacy by connecting people to governing institutions. Legislative support tools, as developed by PopVox, help overwhelmed legislators process precedent and frame decisions. Implementation tools can increase efficiency in service delivery and enforcement. But Allen emphasized that the design principle for implementation should not be efficiency alone. It must also include openness, accountability, and transparency.
Allen also advocated for framing AI as “human complementing, not human replacing,” citing Pennsylvania Governor Shapiro’s policy requiring this in state government. Such framing does educational work by helping people understand the choices in front of society and can develop political counterweight to concentrated technology company power.
Ziyaad Bhorat from Mozilla pushed the structural argument further. If society builds concentrated stacks of consolidated power in private corporations, it should not expect to avoid democratic backsliding. Technological structures shape social and political structures. This is why Mozilla emphasizes openness as both a technical and political principle. Open systems allow auditing, red-teaming, and distributed innovation. Bhorat dismissed arguments that proprietary systems are more secure or economically viable as “garbage,” since open architectures provide security benefits that closed systems cannot match.
Mozilla published a trustworthy AI report in 2020, before the ChatGPT explosion, anticipating that AI would reshape society. The foundation now advocates for “public AI,” meaning systems oriented toward public goods and community benefit rather than reducing citizens to passive users. Bhorat criticized the “move fast and break things” ethos as pernicious, arguing that true innovators think at the systems level across both technical and ethical domains. He cited Kenya’s M-Pesa as an example of how resource constraints can drive innovation, and advocated for governments and philanthropies to deploy funds with the same risk tolerance that venture capitalists show. This would enable grassroots and civil society actors to experiment with new approaches rather than waiting for scaled corporate solutions.
Competing Frameworks
Arts and cultural institutions, Bhorat noted, are underrepresented in AI discussions despite asking fundamental questions about purpose. Why do we want AI? What life do we want to lead? Hollywood’s labor disputes over AI show where these questions become concrete, challenging notions of what art, imagination, and creativity are.
Slosser proposed another mechanism for countervailing power in the form of data unions. These would function not primarily as mechanisms for compensation, but as sources of political leverage. If workers and citizens collectively organize around the data that trains AI systems, they might gain bargaining power that individual users lack. This remains more concept than practice, but it represents an attempt to find leverage points beyond traditional regulation.
Several participants offered theoretical frameworks for thinking about these challenges. Ghebreab drew on Paulo Freire’s “Pedagogy of the Oppressed” and “Pedagogy of Liberation.” Freire describes a “cycle of socialization” in which people are born into assumptions and structures that are reinforced through education and institutions. Much AI technology, Ghebreab suggested, has been socialized in this same cycle, reproducing existing power structures rather than challenging them. The alternative is a “cycle of liberation” focused on empowerment, community building, and collective transformation. If AI is approached with the aim of enabling this transition, ethical questions become design requirements rather than afterthoughts. Freire’s distinction between the “ethics of the market” and the “ethics of life” maps onto the tensions the panel discussed throughout.
Pierre Lévy offered a different framing, placing AI within a broader information ecosystem. People create information that feeds digital memory, which trains AI, which empowers people to create new information in a continuous loop. Responsibility for AI’s effects therefore extends to everyone who creates information that feeds training data. Journalists, professors, researchers, and those maintaining institutional websites bear particular responsibility because AI models weight authoritative sources more heavily. Bad actors understand this dynamic and exploit it by creating websites, infiltrating Wikipedia, or using traditional media to inject information that will influence AI outputs. Lévy advocated for education focused on critical thinking, personal memorization rather than delegation to machines, and awareness of how individual contributions to digital memory shape collective AI outputs.
Allen invoked the concept of “varieties of capitalism,” noting that the question is not capitalism versus alternatives but rather which variety of capitalism a society chooses, and thus which regulatory approaches shape the economy. She argued that policymakers should ask how to optimize for multiple values simultaneously rather than defaulting immediately to trade-off analysis.
These frameworks, including Freire’s liberation cycle, Lévy’s information ecosystem, Allen’s varieties of capitalism, Slosser’s data unions, and Bhorat’s open ecosystems, offer ways of thinking about AI governance. But the translation from framework to operational policy remained largely unexplored. The discussion generated more diagnosis than prescription.
Adaptive and Inclusive AI Governance
Frincy Clement pointed to what is missing in current governance, namely adaptive capability. Existing frameworks lack flexibility to respond to risks not yet known. While large corporations have resources to build responsible AI frameworks, small and medium businesses often lack capacity to understand what responsible AI means or how to implement it. Clement also noted that despite years of initiatives focused on education and workforce development, the percentage of women in technical roles has remained largely static, moving only one or two percentage points over a decade. She cited Dr. Joy Buolamwini’s research on facial recognition failures as an example of how diverse perspectives can surface hidden harms, and mentioned recent backlash against diversity budgets while arguing that if marginalized communities were intentionally excluded historically, intentional policies are needed to bring them back.
Ponce Del Castillo noted that Europe is not alone in developing governance frameworks. UNESCO, the OECD, and the Council of Europe have issued recommendations. The International Labour Organization is planning an observatory on AI in platform work. US state-level law is developing. Trade unions, she suggested, could participate in AI risk assessment by drawing on their experience with occupational health and safety. They could serve as “data representatives” helping determine which AI systems qualify as high-risk. Workers could be involved in conducting fundamental rights impact assessments. Collective agreements can model the provisions of horizontal frameworks like the AI Act for specific sectors, since not all workers are exposed to AI systems in the same way.
To conclude
The panel ended without resolution. The tensions it surfaced, between economic competition and ethical integration, between user empowerment and structural concentration, between theoretical frameworks and operational policy, remain live. The principle that AI should complement rather than replace human work was widely endorsed, but no one demonstrated how to ensure this in practice. The empirical evidence that user-focused interventions fail to prevent influence went largely unaddressed. The concern that AI systems extract tacit knowledge from workers before displacing them produced no policy response beyond sector-specific collective bargaining.
What the panel made clear is that the gap between human-centered AI as rhetoric and human-centered AI as practice is not closing on its own.
