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UN opens Global Dialogue on AI Governance with call for inclusive and evidence-based cooperation

The United Nations opened its first Global Dialogue on AI Governance in Geneva, calling for inclusive, evidence-based and practical international cooperation to ensure that AI supports development while addressing risks related to safety, inequality, disinformation, children’s rights and human oversight.

The inaugural Global Dialogue on AI Governance is taking place on 6–7 July, alongside the AI for Good Global Summit and the WSIS Forum. Established in 2025, the dialogue is intended to provide a platform for governments and relevant stakeholders to discuss international cooperation, share good practices and support open, transparent and inclusive discussions on AI governance.

Opening the session, Ambassador Egriselda López of El Salvador, one of the dialogue’s co-chairs, described the meeting as the beginning of a broader process rather than a one-off event. She said Geneva should be seen not only as a place of arrival, but as a point of departure for continued work on AI governance.

López stressed that meaningful participation requires more than a seat in the room. Countries also need skills, infrastructure, financing, institutions and partnerships to shape and benefit from AI. Her co-chair, Ambassador Rein Tammsaar of Estonia, said AI is already affecting every country, regardless of its level of technological development, and that governance discussions must therefore include all regions, levels of development and relevant stakeholders.

UN Secretary-General António Guterres warned that AI is advancing at ‘runaway speed’ and is being deployed faster than institutions can manage. He said AI is already reshaping economies, labour markets, elections and security, while society is facing what he described as an experiment being run ‘without a plan’ and ‘without consent’.

Guterres identified three major risks highlighted by scientific evidence: the speed of AI deployment, the concentration of power in a small number of companies and countries, and the erosion of truth through AI-enabled misinformation. He warned that computing power, data and talent remain concentrated, leaving many countries, particularly developing ones, with limited influence over technologies that may shape their futures.

At the same time, Guterres emphasised AI’s potential to support development, including in healthcare, education and agriculture. If shared widely, he said, AI could help make expertise more accessible and become a ‘great equaliser’ of the twenty-first century.

The Secretary-General outlined four priorities for international action: common safety standards, clear red lines grounded in human rights, stronger capacity-building for developing countries and greater transparency about AI’s environmental footprint. He also called for an AI child safety pledge, a global fund and network for AI capacity-building, and an international legal ban on lethal autonomous weapons, which he referred to as ‘killer robots’.

Annalena Baerbock, President of the UN General Assembly, said AI is developing at a pace that does not allow governments the time they had with earlier technological revolutions. She argued that AI cannot be governed by a few actors alone and must be addressed through the UN with participation from all countries and stakeholders.

Baerbock also highlighted harmful uses of AI, including deepfakes and gendered abuse. She said such abuses disproportionately target women and girls and described them as part of a broader challenge to human rights. At the same time, she pointed to AI’s potential to support the Sustainable Development Goals, including through disaster warning, agriculture, health and education.

Doreen Bogdan-Martin, Secretary-General of the International Telecommunication Union, framed the opening as part of a wider ‘Geneva Digital Week’ that brings together the Global Dialogue on AI Governance, the work of the Independent International Scientific Panel on AI, the AI for Good Global Summit and the WSIS Forum. She contrasted the current pace of AI governance discussions with the early years of the internet, noting that the UN has moved more quickly to convene global dialogue on generative AI.

Khaled El-Enany of UNESCO focused on implementation, saying that a gap remains between principles and practice. He highlighted UNESCO’s 2021 Recommendation on the Ethics of Artificial Intelligence as a global standard for aligning AI with human rights, sustainability and inclusion. He said UNESCO is supporting more than 80 countries in strengthening legal frameworks, institutional capacities and accountability mechanisms, and noted that over 50,000 civil servants and judicial actors have benefited from UNESCO-supported AI training.

El-Enany also said UNESCO is launching a collective reflection on a new global normative instrument to safeguard children and young people in the age of AI and digital technologies.

Amandeep Singh Gill, UN Under-Secretary-General and Special Envoy for Digital and Emerging Technologies, underlined the scale of participation in the dialogue, noting representation from more than 170 countries alongside scientists, entrepreneurs, civil society, international organisations and technical communities. He said inclusion in AI governance cannot be treated as a one-off exercise, adding that without capacity, ‘dialogues are monologues and science is just abstract’.

Singh Gill situated the dialogue within a longer UN process that includes the High-Level Panel on Digital Cooperation, the Roadmap for Digital Cooperation, the Global Digital Compact and the High-Level Advisory Body on AI. He said the process would continue with a second round in New York next year, expected to be held alongside the STI Forum.

The opening session showed broad agreement that AI governance should be inclusive, evidence-based, rights-oriented and supported by practical capacity-building. Speakers repeatedly stressed that AI’s potential benefits for development, education, health and agriculture must be matched by safeguards on safety, accountability, children’s rights, truth, environmental sustainability and human oversight.

Tammsaar closed the opening by saying the discussion had highlighted both AI’s opportunities and the need for stronger international cooperation to ensure that the technology contributes to sustainable development, inclusion and shared prosperity. The meeting then moved to the presentation of the preliminary report of the Independent International Scientific Panel on Artificial Intelligence.

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WSIS Forum 2026 explores how the IGF should evolve after gaining a permanent mandate

The future of the Internet Governance Forum (IGF) took centre stage at the WSIS Forum 2026, where policymakers, former diplomats, technical experts and internet governance practitioners discussed how the forum should evolve following the UN’s decision to grant it a permanent mandate.

Speakers agreed that the challenge is no longer whether the IGF should continue, but how it can become more relevant, effective and responsive to emerging issues such as AI while preserving its multistakeholder character. The discussion focused on four broad priorities, such as strengthening government participation, improving intersessional work, deepening links with national and regional IGF initiatives (NRIs), and ensuring the forum has sufficient institutional capacity and sustainable funding.

Governments need a stronger role without changing the IGF’s character

A recurring theme was how to increase meaningful government participation without transforming the IGF into a traditional intergovernmental negotiation forum.

Anriette Esterhuysen, human rights defender and computer networking pioneer from South Africa, argued that governments must participate more actively, particularly to strengthen digital policymaking in developing countries, but warned against reducing their involvement to formal speeches by senior officials.

Instead, she said governments should engage openly on practical policy challenges that require collaboration with the wider internet governance community.

Former Latvian ambassador Janis Karklins echoed this view, arguing that governments would only dedicate time and resources to the IGF if it addressed issues directly relevant to their national priorities.

Planning for the upcoming IGF in Nairobi, he suggested, should take into account the policy needs of African governments to ensure the forum delivers practical value.

Jennifer Chung, Chair of the Multistakeholder Advisory Group (MAG), also stressed that the initiative should be understood as a ‘government dialogue with stakeholders’ rather than a separate government track, preserving the IGF’s long-standing multistakeholder model.

Meanwhile, IGF Programme and Technology Manager Chengetai Masango said discussions on the exact format remain ongoing, with organisers considering how the dialogue could build on existing high-level sessions rather than creating an entirely new structure.

Stronger outcomes through year-round collaboration

Participants also debated how the IGF could produce more tangible results while remaining a platform for dialogue rather than negotiations.

Konstantinos Komaitis opened the discussion by asking how the IGF could move beyond its reputation as a ‘talking shop’ without becoming another UN negotiating process.

Esterhuysen argued that achieving greater impact requires changing the way the IGF works rather than changing its mandate. She suggested more structured intersessional work, thematic synthesis and longer-term collaboration on priority issues instead of relying primarily on standalone workshops during the annual meeting.

Andrea Calderaro, Director of Cyber Diplomacy at the EU Institute for Security Studies (EUISS), similarly argued that the most valuable work happens between annual IGF meetings, with governments and stakeholders conducting national consultations and bringing those experiences into global discussions.

Masango defended dialogue as the forum’s core purpose, but agreed that stronger follow-up and more practical outputs are needed. He said previous initiatives, including voluntary commitments, had not always been sufficiently tracked or incorporated into future work.

National and regional IGFs seen as a growing strength

Speakers also highlighted the growing importance of national, regional and youth Internet Governance Forums, which now number more than 180 worldwide.

Esterhuysen welcomed their explicit recognition in the WSIS+20 outcome document, describing them as one of the IGF’s greatest successes.

Chung said the relationship between the global IGF and NRIs should evolve beyond annual event coordination towards continuous thematic collaboration and shared learning throughout the year.

She noted particularly strong growth among youth initiatives, especially in Africa and Asia, arguing that younger participants increasingly want meaningful involvement in shaping Internet governance discussions rather than symbolic participation.

Esterhuysen proposed a two-way model in which the global IGF identifies concrete policy questions, NRIs and intersessional groups examine them throughout the year, and the Secretariat synthesises the results into practical, non-negotiated policy options for governments and other stakeholders.

Permanent mandate brings new expectations

The discussion also touched on longer-term institutional questions, including funding and Secretariat capacity.

Although speakers acknowledged that financial sustainability remains an important challenge, they agreed that the immediate priority is preparing a successful IGF meeting in Nairobi while gradually implementing reforms in the years ahead.

Calderaro argued that the IGF should increasingly serve as a hub connecting the growing number of international digital governance processes rather than functioning only as an annual conference.

Esterhuysen also urged the forum to become more willing to address politically sensitive issues, including corporate accountability, arguing that its permanent mandate provides an opportunity to take on more substantive policy debates.

Closing the session, participants broadly agreed that the IGF’s future lies not in becoming a negotiating body, but in strengthening dialogue, improving policy-relevant outputs, deepening collaboration across national and regional initiatives, and ensuring governments, civil society, academia, the private sector and technical communities remain equally engaged as internet governance continues to evolve.

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Stronger health data governance seen as key to trusted AI and digital health at WSIS Forum 2026

Strong legislative frameworks for health data governance are becoming essential to ensure that AI and digital health technologies remain trustworthy, equitable and rights-based, speakers said during a session at the WSIS Forum 2026.

The discussion brought together representatives from governments, international organisations, civil society and the private sector, who agreed that while AI and digital technologies are transforming healthcare, governance frameworks have not always kept pace. Speakers repeatedly argued that stronger legislation, greater international coordination and broader stakeholder participation will be necessary to build public trust and enable responsible data sharing across borders.

The session formed part of the WSIS Forum 2026, held in Geneva from 6 to 10 July. Co-organised by the International Telecommunication Union (ITU), UNESCO, UNDP and UNCTAD together with more than 50 UN organisations, the forum serves as one of the UN’s principal multistakeholder platforms for digital cooperation and sustainable development.

Trust begins with governance

Opening the discussion, Mathilde Forslund of Transform Health argued that health data has become the foundation of modern healthcare, powering everything from patient care and disease surveillance to AI innovation and health system planning.

However, she stressed that technological progress alone is insufficient.

‘Digital technologies and AI are transforming health systems rapidly, but these benefits will only be realised equitably and responsibly if governance keeps pace and public trust is maintained,’ she said.

Forslund argued that trusted governance requires legislation grounded in human rights, transparency and equity, alongside inclusive decision-making that informs citizens how their health data is collected, shared and protected. She also called for stronger national legal frameworks governing both health data and AI while encouraging greater regional and international alignment to prevent fragmented rules from undermining interoperability and cross-border cooperation.

Rather than starting from scratch, she noted that countries can already build on existing resources, including Transform Health’s Health Data Governance Principles, WHO guidance on AI, OECD recommendations and emerging regional initiatives such as the European Health Data Space (EHDS) and the Africa CDC’s work on continental health data governance.

National legislation provides legal certainty

Drawing on Zambia’s experience, Andrew Kashoka, Director of Information Technology at the Ministry of Health of Zambia, explained that governments increasingly recognise the need for legal certainty as digital health systems expand.

He argued that while policies and strategies provide direction, legislation ultimately establishes enforceable rights and obligations governing consent, privacy, accountability and access to health data.

‘Technology moves faster than policy and policy moves faster than legislation,’ Kashoka observed.

He described Zambia’s National Digital Health Strategy and the country’s participation in the WHO Global Initiative on Digital Health (GUIDE), noting that electronic health records, digital public infrastructure and AI all require strong legal foundations to maintain public confidence.

Kashoka also highlighted the Africa CDC’s continental health data governance framework, saying it provides African countries with shared principles that support legal interoperability, trusted cross-border collaboration, regional disease surveillance and responsible AI innovation.

Coordination, not policy, remains the biggest challenge

Several speakers suggested that governance challenges stem less from the absence of policies than from fragmented implementation.

Linda Bonyo, Founder of the Lawyers Hub and the Africa AI Policy Lab, argued that numerous organisations are already developing health data and AI governance initiatives, but often work independently with limited coordination.

She criticised the exclusion of parliaments and judicial institutions from governance discussions, arguing that legislators and courts play essential roles in creating and interpreting legal frameworks.

Bonyo also called for stronger institutional capacity, particularly among national data protection authorities that increasingly find themselves overseeing AI without sufficient technical expertise or financial resources.

She further highlighted practical barriers limiting African participation in international governance discussions, including visa restrictions and the high cost of attending Geneva-based meetings.

Summarising the challenge, Bonyo remarked that the problem is ‘not a policy problem… it’s implementation,’ urging countries to develop governance frameworks rooted in local realities rather than simply adopting foreign regulatory models.

Private sector and technical standards also matter

Representing the technical and private-sector perspective, Simão Ferraz de Campos Neto, Senior Counsellor at the International Telecommunication Union (ITU), argued that clearer rules and common technical standards are essential if health data is to be shared safely without discouraging innovation.

He noted that organisations frequently hesitate to share data not because they oppose collaboration, but because legal uncertainty creates concerns about liability.

Campos Neto called for interoperable technical standards, machine-readable datasets and standardised data-sharing agreements that could make trusted health data exchange significantly easier.

He also cautioned against treating AI as a single technology requiring uniform regulation.

Instead, he advocated proportionate, risk-based regulation that reflects the diversity of AI applications, while avoiding excessive regulatory burdens that could slow innovation.

Momentum builds towards global action

Closing the discussion, Jamal Alshanfari, Ambassador and Head of Oman Health office in Geneva, pointed to growing political momentum following discussions at the World Health Assembly, where member states expressed broad support for developing stronger global health data governance arrangements.

He identified four priorities for the next phase of work. The phases are expanding international consensus, strengthening national legislation and institutional capacity, providing practical implementation guidance, and ensuring that governments, civil society, academia, industry and end users all participate in shaping future frameworks.

Alshanfari also reminded participants that governance discussions should ultimately focus on those most affected by digital health technologies.

‘Everybody forgets about the end user,’ he said, stressing that trust depends on governance frameworks serving citizens as much as institutions.

In her closing remarks, Forslund said the discussion demonstrated encouraging progress across national, regional and global initiatives, while acknowledging that implementation remains the greatest challenge. She pointed to the upcoming World Health Assembly as an important opportunity to advance work on a possible global resolution on health data governance.

The session concluded with broad agreement that trusted AI in healthcare will depend not only on technological innovation but also on stronger legislation, greater international coordination, practical implementation, and governance frameworks that place citizens’ rights and public trust at their centre.

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What Geneva’s history can teach us about governing AI

Based on a Diplo interview with Jovan Kurbalija, Executive Director of Diplo, conducted by Maricela Muñoz.

Few places embody the history of international diplomacy as vividly as Geneva’s Alabama Room. It was here, in 1864, that representatives of European states signed the first Geneva Convention, laying the foundations of modern international humanitarian law. The same room also hosted negotiations that resolved the Alabama Claims, an arbitration between the United States and the United Kingdom that became a landmark in the peaceful settlement of international disputes.

More than 160 years later, the room continues to host conversations about another challenge with global implications – AI. While the technologies have changed dramatically, the underlying questions remain remarkably familiar. How can societies govern transformative innovations responsibly? How can competing interests find common ground? And how can international cooperation keep pace with technologies evolving faster than regulation?

These themes formed the basis of a recent Diplo interview with Jovan Kurbalija, Executive Director of Diplo, who reflected on Geneva’s historical legacy and its continuing relevance for AI governance. His central argument is that understanding the future of AI requires more than technical expertise. It also requires revisiting the intellectual traditions, diplomatic culture, and human values that have shaped Geneva for centuries.

History offers principles, not ready-made answers

Kurbalija cautions against treating history as a collection of simple solutions.

‘History does not provide us ready-made lessons. Our moment is unique in many respects.’

Instead, history provides something more enduring, the principles that continue to guide societies confronting new challenges.

Standing inside the Alabama Room, Kurbalija described history as something that ‘echoes across time.’ Rather than searching for direct historical parallels, he suggested imagining the negotiators who once walked through Geneva’s streets before gathering around the same table to discuss humanitarian protection or peaceful dispute settlement.

The technologies confronting today’s diplomats are different, yet many of the qualities that enabled successful negotiations remain unchanged. Patience, dialogue, respect for opposing views, and the willingness to seek common ground continue to underpin effective diplomacy.

As governments, international organisations, companies, researchers, and civil society grapple with AI governance, these diplomatic traditions may be more relevant than ever.

Geneva’s enduring values: Inclusion and compromise

For Kurbalija, Geneva’s importance extends well beyond the concentration of international organisations located around the city.

Its defining contribution lies in a diplomatic culture built around inclusion and compromise.

Inclusion has long characterised Geneva’s approach to international negotiations. Whether discussing humanitarian law in the nineteenth century or AI governance today, meaningful outcomes depend on ensuring that all those affected have a voice.

That principle has become particularly important for AI governance.

‘We should have AI companies, but we must have governments, communities, citizens, marginal groups all over the world.’

The observation reflects one of the central challenges of AI governance. Decisions about AI increasingly affect education, healthcare, employment, security, trade, and human rights. Consequently, discussions cannot remain confined to governments and technology companies alone.

Kurbalija identifies compromise as the second defining Geneva principle.

‘Compromise is not a very popular word today.’

Yet he argues that compromise represents an ethical strength rather than a weakness. It requires recognising that different actors hold legitimate interests and finding solutions that, while imperfect, remain acceptable to everyone involved.

In an era increasingly shaped by geopolitical competition over AI, these principles may prove as valuable as any technological breakthrough.

EspriTech de Genève: When history speaks to AI

One of the interview’s most distinctive ideas is Kurbalija’s concept of EspriTech de Genève.

Drawing inspiration from the traditional Esprit de Genève, which reflects the city’s humanitarian and diplomatic heritage, EspriTech de Genève explores how thinkers associated with Geneva anticipated many of today’s debates about technology, knowledge, and humanity.

Rather than beginning with computers, Kurbalija traces AI governance back through centuries of philosophy, literature, linguistics, and science.

Art, Collage, City, Metropolis, Urban, Water, Waterfront, Adult, Male, Man, Person, Nature, Outdoors, Scenery, Female, Woman, Face, Head, Accessories, Glasses, Transportation, Vehicle, Yacht, Architecture, Building, Monument, Arch, Jean Piaget, Mary Wollstonecraft Shelley, Jorge Luis Borges, Ferdinand de Saussure, Jean-Jacques Rousseau, Valentin Haüy, Voltaire, Jehan Cauvin

Mary Shelley’s Frankenstein, written near Geneva more than two centuries ago, provides perhaps the most familiar example. The novel tells the story of a scientist whose creation ultimately escapes his control.

‘It is the eternal reminder of the human drive to push the frontier, to invent, to discover new things—and at the same time the human predicament that the very invention we developed could hurt humanity.’

For Kurbalija, the novel remains strikingly relevant as societies debate increasingly capable AI systems. The question is no longer simply whether humans can build powerful technologies, but how they can ensure those technologies remain aligned with human interests.

Another recurring influence is Argentine writer Jorge Luis Borges, whose works explored uncertainty, knowledge, and the limits of human understanding. Reflecting on Borges’ observation that humanity must continue building ‘as if the sand were stone,’ Kurbalija argues that uncertainty is not a flaw to eliminate but a defining feature of human existence.

Attempts to achieve complete certainty through technology, he suggests, risk repeating an ancient mistake, believing that humans can fully master complexity.

Rousseau, Bonnet and Saussure: forgotten foundations of the AI age

The interview also revisits several Genevan thinkers whose ideas continue to resonate in discussions about AI.

Jean-Jacques Rousseau’s concept of the social contract raises questions about human agency in an increasingly digital society. If knowledge becomes concentrated within a handful of large AI systems, Kurbalija argues, societies may need to reconsider how citizens exercise autonomy, participate in democratic life, and realise their potential.

Charles Bonnet, an eighteenth-century Genevan natural philosopher, appears as an unexpectedly modern figure. Fascinated by recurring patterns in nature, Bonnet studied the mathematical organisation of leaves and explored how seemingly complex biological systems emerge from underlying structures.

According to Kurbalija, Bonnet’s search for patterns anticipated, in remarkably abstract form, today’s machine learning systems, which likewise identify statistical relationships within vast quantities of information.

Language itself forms another bridge between Geneva’s intellectual history and contemporary AI.

Swiss linguist Ferdinand de Saussure transformed linguistics by distinguishing between the structure of language and its meaning. Although writing decades before computers existed, his work laid conceptual foundations that later influenced computational linguistics and, indirectly, today’s large language models.

‘If AI companies ever had to pay royalties for ideas,’ Kurbalija jokes, ‘Saussure’s successors would probably earn quite a bit.’

Behind the humour lies a serious point, that AI did not emerge in an intellectual vacuum. It builds upon centuries of inquiry into language, knowledge, communication, and human cognition.

Human-centred AI begins with human values

Throughout the conversation, Kurbalija repeatedly returns to one theme, that AI governance is ultimately about people rather than machines.

The phrase ‘human-centred AI’ appears frequently in international discussions, yet he argues that its meaning deserves closer examination.

What does it actually mean to place humans at the centre of AI? For Kurbalija, the answer lies in humility.

Drawing once again on Frankenstein, he argues that technological ambition should always be accompanied by recognition of human limitations.

‘We should have humility,’ he says.

Rather than pursuing AI for its own sake, societies should ask how technology can support human dignity, creativity, education, and well-being.

He also highlights the principle of subsidiarity, the idea that decisions should be taken as closely as possible to the people affected by them. Applied to AI, this means involving citizens, educators, local communities, researchers, and smaller organisations alongside governments and major technology companies.

Broad participation, he argues, helps ensure that AI is perceived not as an external force imposed upon society, but as a tool developed with society.

Geneva’s next chapter

Geneva’s role in AI governance continues to evolve.The city already hosts initiatives such as the AI for Good Global Summit, the World Summit on the Information Society (WSIS), and numerous discussions on AI governance involving governments, international organisations, academia, civil society, and the private sector.

It is also expected to host the AI Summit in 2027, further reinforcing its position as one of the world’s principal centres for international dialogue on emerging technologies.

Yet Kurbalija believes Geneva’s greatest contribution lies not in the number of meetings it convenes but in the diplomatic culture it represents.Its traditions of inclusion, dialogue, compromise, and respect for human dignity offer an important counterbalance at a time when AI discussions are increasingly shaped by geopolitical competition, technological rivalry, and commercial pressures.

He concludes the interview with three messages for policymakers:

The first is to avoid what he calls ‘chrono-narcissism’, the belief that every challenge is entirely new and disconnected from history.
The second is to approach AI with humility, recognising both its extraordinary potential and its inherent risks.
The third is to ensure that AI governance remains genuinely inclusive by bringing decision-making closer to the people whose lives the technology will affect.

These principles echo far beyond Geneva.

As AI becomes embedded in nearly every aspect of society, debates about governance are becoming less about technology itself and more about the values that should guide its development. In that respect, Geneva’s greatest contribution may not be a particular regulatory model or institutional framework, but a reminder that diplomacy, dialogue, and humanity remain as essential in the AI era as they were when the first Geneva Convention was signed more than a century and a half ago.

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Altman proposes US-led international forum for AI safety standards

OpenAI CEO Sam Altman has called for the creation of a US-led international forum to establish global safety standards for AI, arguing that no single country or company should dominate the governance of increasingly capable AI systems.

Writing in an opinion article published in the Financial Times, Altman proposed an international body bringing together governments, independent technical experts, and other stakeholders to develop accepted AI safety standards, provide impartial assessments of AI capabilities and risks, and make advanced AI technologies available to countries and organisations that participate in and comply with agreed rules.

According to Altman, such a forum could also serve as a governance mechanism for frontier AI developers, helping to reduce commercial pressures that may encourage companies to prioritise rapid deployment over safety. He argued that international cooperation has previously enabled countries to manage other strategically important technologies despite geopolitical competition.

To illustrate his proposal, Altman pointed to existing international governance mechanisms such as the International Atomic Energy Agency (IAEA), which oversees the peaceful use of nuclear technology, as well as global aviation safety frameworks and international financial standards. In his view, these models demonstrate that countries can establish common rules for technologies with significant cross-border implications while maintaining national interests.

Altman also argued that the benefits of AI should be shared more broadly, writing that ‘everyone on Earth should benefit from this technology and determine for themselves how best to use it.’ His proposal follows discussions at the recent Group of Seven (G7) summit in France, where executives from OpenAI, Anthropic, and Google DeepMind met with political leaders to discuss international approaches to governing advanced AI models.

A key challenge for any international oversight mechanism, however, remains enforcement. Unlike nuclear facilities or aircraft, frontier AI models are developed within highly secured data centres, making independent verification considerably more difficult. The limited visibility into model training, testing, and deployment has led many experts to question how compliance with international AI standards could be monitored in practice.

Altman’s proposal is not the first call for stronger international oversight of advanced AI. OpenAI and Anthropic have previously supported the idea of international governance mechanisms for frontier AI systems. Anthropic CEO Dario Amodei has argued for a more prescriptive regulatory approach, drawing comparisons with the US Federal Aviation Administration and advocating stronger regulatory oversight for highly capable AI models.

The proposal also comes as governments continue to expand their involvement in AI governance. Alongside national regulatory initiatives, international discussions have accelerated through forums such as the G7, the Global Partnership on AI (GPAI), and the UN.

Earlier this week, the UN’s Independent International Scientific Panel on Artificial Intelligence published its first preliminary assessment of AI opportunities, risks, and governance challenges ahead of the inaugural Global Dialogue on AI Governance in Geneva, reflecting growing international efforts to establish evidence-based approaches to AI governance.

Whether Altman’s proposal develops into a formal international initiative will ultimately depend on governments rather than AI companies. Commenting on broader discussions around AI governance, analysts at the Brookings Institution argued that cooperation between governments and leading AI developers could help establish common standards, but stressed that any future international framework would need effective implementation and enforcement mechanisms rather than relying solely on voluntary commitments.

As governments, international organisations, and AI developers continue debating how to govern increasingly capable AI systems, Altman’s proposal adds to a growing conversation about whether existing institutions are sufficient or whether new international mechanisms will be needed to manage the opportunities and risks associated with frontier AI.

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University of Wisconsin launches College of Computing & AI

The University of Wisconsin-Madison has launched its College of Computing & Artificial Intelligence (CAI), the institution’s first new college in more than four decades.

The new college brings together the departments of Computer Sciences, Statistics and the Information School, building on the School of Computer, Data & Information Sciences established in 2019.

The college will focus on computing and AI education and research while promoting collaboration across fields including health, engineering, business, the social sciences, the arts and the humanities.

The university also plans to launch new academic programmes, recruit 50 faculty members over the coming years and expand partnerships with industry and government to strenthen research, education and innovation.

Why does it matter?

The creation of a dedicated College of Computing & Artificial Intelligence reflects the growing importance universities are placing on AI as a cross-disciplinary field rather than a specialised area within computer science. By bringing together expertise from multiple disciplines, the university aims to prepare students and researchers to address the technical, social and ethical challenges of AI.

The investment also highlights intensifying competition among higher education institutions to attract talent, research funding and industry partnerships in AI. Expanding faculty, academic programmes and collaboration with government and business positions the university to play a larger role in developing the next generation of AI research and workforce skills.

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Claude Science launches AI workbench for researchers

Claude Science has been launched as an AI workbench designed to streamline scientific research by bringing data analysis, coding and research tools into a single integrated environment. The platform is designed to help researchers analyse data, run multi-step workflows, and generate publication-ready outputs with full transparency.

The platform consolidates research tools such as databases, coding environments and analysis software, enabling scientists to work across disciplines without switching between applications. Outputs are fully auditable, with embedded code, workflow histories and documentation to support validation and reproducibility.

Claude Science also uses a multi-agent architecture comprising specialist agents and a reviewer agent that verifies calculations and citations. It can be deployed on local infrastructure or high-performance computing systems, allowing institutions to scale AI-assisted research while keeping sensitive data within their own environments.

Why does it matter?

Claude Science reflects a broader evolution of AI from a standalone assistant to an integrated research platform. By combining specialised AI agents, computational tools and transparent workflows in a single environment, it aims to simplify scientific research while improving reproducibility and collaboration.

The platform also raises broader questions about the future of AI in science. As researchers increasingly rely on AI to support data analysis and experimentation, ensuring transparency, validation and institutional control over sensitive research data will be essential to maintaining scientific integrity and trust.

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OpenAI launches GeneBench-Pro for AI biology research

OpenAI has introduced GeneBench-Pro, a research benchmark designed to assess whether AI agents can perform the complex, judgment-intensive analysis required in real-world computational biology.

Unlike conventional benchmarks that focus on factual recall or routine workflows, GeneBench-Pro is designed to measure what OpenAI calls ‘research taste‘, the sequence of judgement calls involved in scientific analysis, from interpreting ambiguous data and revising assumptions to deciding whether findings are robust enough to inform downstream research.

The benchmark comprises 129 problems spanning ten domains within computational biology, including statistical genetics, cancer genomics, clinical diagnostics, and pharmacogenomics. Each problem presents an AI agent with a realistic and deliberately messy dataset, brief experimental context, and a target to estimate.

To answer correctly, the model must explore the data iteratively, select an appropriate analytical approach, and supply a final answer without exploiting shortcuts or matching arbitrary author preferences. To prevent common benchmark shortcuts, every problem uses synthetically generated data whose underlying causal structure is fully known, allowing performance to be measured against a controlled ground truth.

OpenAI said its flagship model, GPT-5.6 Sol, achieved a pass rate of 28.7% at the highest reasoning setting, increasing to 31.5% in Pro mode. By comparison, the strongest model available when the original GeneBench was introduced scored below 5%.

External reviewers estimated that completing a typical GeneBench-Pro task would require 20 to 40 hours of expert work and cost thousands of dollars, whereas AI inference currently costs only a few dollars per run. OpenAI argues this suggests substantial economic potential even before models achieve expert-level performance.

OpenAI acknowledged that frontier models still solve fewer than one-third of the benchmark problems, often making partial progress but failing to complete the full chain of scientific reasoning expected from experienced researchers. To encourage independent evaluation, the company is open-sourcing ten representative tasks on Hugging Face and providing a 50-question subset to Artificial Analysis for third-party benchmarking.

Why does it matter?

GeneBench-Pro reflects a broader shift in AI evaluation from testing factual knowledge and coding ability to assessing whether models can support complex scientific reasoning. As computational biology increasingly becomes limited by data interpretation rather than data generation, reliable AI assistance in analytical workflows could accelerate research in areas such as genomics, drug discovery and precision medicine.

The benchmark also highlights the importance of rigorous evaluation methods for frontier AI. By using controlled synthetic datasets with known ground truth, GeneBench-Pro seeks to measure not only whether models reach the correct answer but also how well they make the sequence of judgements required in real-world scientific research.

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Women and AI: Reflecting bias or reinforcing inequality?

Ask an image-generation model to create a CEO, a software engineer, or a successful entrepreneur, and chances are the result will be male. Ask for a nurse, a personal assistant, or a caregiver, and a woman is far more likely to appear.

Such outputs have fuelled growing concerns about gender bias in AI and the broader relationship between women and synthetic intelligence. Yet a more complicated question lies beneath the surface: are AI systems creating these stereotypes, or are they simply learning them from society?

AI learns patterns, not values

AI is not neutral; it learns from historical and social data. From books and news archives to websites, social media posts, and workplace statistics, modern AI systems are trained on enormous quantities of human-generated content. If society has historically associated men with leadership and women with caregiving, AI is likely to learn those associations as statistical patterns. The real challenge emerges when these patterns are reproduced millions of times every day, shaping perceptions of what is normal, expected, or achievable.

The debate surrounding gender bias in AI is therefore not only about technology. It is also about how existing inequalities are translated into digital systems and whether AI ultimately reinforces or challenges them.

Women and AI, gender bias — image via Magnific

How AI systems learn and reproduce gender bias

AI has often been portrayed as objective, rational, and free from human prejudice. Reality is more complicated. Machine learning models do not distinguish between desirable and undesirable social patterns. Their purpose is to identify relationships within data and use them to make predictions or generate outputs.

A landmark 2017 study published in Science demonstrated that AI language models learned many of the same implicit biases found among humans. Researchers discovered that word associations frequently linked men with careers, science, and leadership, while women were more closely associated with family and domestic roles. Importantly, the systems were not instructed to adopt these views. They simply learned them from the data available to them.

From a machine-learning perspective, stereotypes are not recognised as stereotypes. They are recognised as recurring patterns.

That distinction matters. AI does not understand concepts such as fairness, equality, or discrimination. It understands probabilities. If particular associations dominate books, websites, news reports, and online discussions, AI systems are likely to absorb those associations and reproduce them in their outputs.

Much of the discussion about women and AI begins here. Gender bias in AI is often less a product of malicious design and more a reflection of the social realities embedded in training data.

How AI amplifies gender stereotypes and inequality

Many experts argue that AI acts as a mirror of society. In some respects, that assessment is correct. If men currently occupy a majority of senior corporate leadership positions, the AI model that frequently depicts CEOs as male may simply be reflecting existing labour-market realities.

However, reflection is only part of the story.

Historically, stereotypes have spread through institutions, media, education systems, and interpersonal interactions. AI introduces a new dynamic because it operates at a scale no individual human can match. Search engines, recommendation systems, chatbots, virtual assistants, and generative AI platforms interact with millions of users simultaneously.

The concern, therefore, is not that AI can be biassed. Humans have always been biassed. The concern is that AI can replicate and distribute those biases with unprecedented speed, consistency, and reach.

A stereotype expressed by one individual has limited influence. A stereotype repeated by an algorithm millions of times can gradually shape expectations about who belongs in positions of authority, innovation, or expertise.

Questions surrounding AI and gender equality extend beyond technical accuracy. Even if an AI system reflects current realities, repeated exposure to those realities may reinforce the perception that they are natural, inevitable, or desirable.

How AI systems portray women and gender roles

Evidence of gender stereotypes in AI has appeared across a wide range of technologies.

Image-generation systems have repeatedly associated women with caregiving and support roles while portraying men as executives, scientists, engineers, entrepreneurs, and political leaders. Similar patterns have emerged in language models, search algorithms, and recommendation systems.

Such outputs raise concerns because representation influences perception. When leadership, technical expertise, and innovation are consistently presented through a male lens, AI may unintentionally reinforce assumptions about gender and professional capability.

Researchers often describe this phenomenon as representational harm. Unlike direct discrimination, representational harm does not necessarily involve financial loss or exclusion from opportunities. Instead, it affects how groups are perceived in society and how individuals understand their own potential.

For younger generations growing up alongside AI-powered technologies, these representations may become part of the digital environment through which social norms are learned. AI increasingly shapes the way people search for information, discover role models, and imagine future careers. As a result, the way women are portrayed by AI systems has implications that extend far beyond the technology sector itself.

The gender bias feedback loop in AI

One of the most important concepts in discussions about gender bias in AI is the feedback loop.

Society creates patterns and inequalities.

These patterns are recorded in digital data.

AI learns from that data.

AI systems reproduce these patterns in their outputs.

People consume these outputs and may internalise them.

New data is generated that reflects the same assumptions.

The cycle then repeats itself.

Viewed through this lens, AI becomes part of a system through which existing inequalities can be continuously reproduced and normalised.

Understanding this feedback loop shifts the debate away from the simple question of whether AI is biassed. A more important question emerges: what happens when social inequalities become embedded in technologies that many people perceive as objective and trustworthy?

That question sits at the heart of contemporary debates surrounding AI ethics, responsible AI development, and digital governance.

Why women in AI governance and development still matter

Discussions about gender bias in AI often focus on the underrepresentation of women in AI and the broader technology sector. While diversity remains an important issue, it should not be viewed as a simple explanation for biassed outputs.

Increasing the number of women working in AI would not automatically eliminate stereotypes from the training data. Models trained on historical information would still learn many of the same social patterns.

However, representation becomes significant at the level of governance.

Decisions about whether biassed outputs should be corrected, contextualised, or left unchanged are ultimately human decisions. Diverse teams may be better positioned to identify harms that homogeneous groups overlook and to challenge assumptions that might otherwise remain embedded in AI systems.

The importance of women in AI, therefore, extends beyond mere representation. It relates to participation in the governance structures that determine how AI is developed, evaluated, and deployed.

The questions about fairness, accountability, and responsible AI are not purely technical. They are social and political questions that require a broad range of perspectives.

The future of gender equality in AI

AI is frequently described as a transformative technology, yet its most disruptive impact may not be what it creates, but what it reveals. For centuries, societies have debated equality through laws, institutions, and cultural norms. AI introduces a different form of scrutiny. By converting human behaviour into data and data into predictions, it exposes patterns that often remain invisible until they are reflected back at scale.

In that sense, debates about women and AI are not merely debates about technology. They are discussions about who gets represented in the collective knowledge, whose experiences become part of the historical record, and which assumptions are treated as facts simply because they have been repeated often enough. As societies increasingly rely on algorithms to organise information and inform decisions, the line between what is statistically common and what is socially acceptable may become one of the defining questions of the digital age.

AI may never tell society what is right. Yet by revealing the patterns embedded in human history, it is forcing a deeper question: when machines learn from us, what exactly are we teaching them?

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European Commission explores scaling AI in agriculture

The European Commission’s Directorate-General for Agriculture and Rural Development (DG AGRI) and Directorate-General for Communications Networks, Content and Technology (DG CONNECT) jointly organised an online expert workshop on 24 June to explore how to accelerate AI adaption and scale trusted AI solutions across the agriculture sector.

The workshop was organised within the framework of the Commission’s Apply AI Strategy, which aims to accelerate AI adoption in strategic sectors, including agri-food, while strengthening European competitiveness, technological sovereignty and uptake among small and medium-sized enterprises. Participants discussed AI applications already being deployed in farm management, precision agriculture, crop and livestock monitoring, advisory services, agricultural robotics and the simplification of administrative processes.

The workshop focused on three priorities: assessing the current level of AI adoption in EU agriculture, identifying barriers to wider deployment and exploring policy measures that could support greater uptake. An interactive session also examined what is needed to ensure AI solutions in agriculture are developed, tested, and validated in a trustworthy and responsible manner.

The workshop’s findings will inform a stakeholder input note identifying priority AI use cases, barriers to adoption, infrastructure and data requirements, and potential follow-up actions under the Apply AI Strategy and related EU programmes supporting the digital transition of agriculture.

Why does it matter?

The workshop illustrates how the European Commission is moving from promoting AI in principle to addressing the practical conditions needed for large-scale deployment. In agriculture, AI has the potential to improve productivity, reduce resource use and simplify administrative tasks, but broader adoption will depend on access to high-quality data, digital infrastructure, trusted solutions and support for farmers and SMEs.

The initiative also reinforces the EU’s wider strategy of linking AI deployment with competitiveness and technological sovereignty. By connecting the Apply AI Strategy with the Common Agricultural Policy, the Common European Agricultural Data Space and Horizon Europe, the Commission is seeking to build an ecosystem in which AI can be adopted responsibly while supporting the long-term digital transformation of Europe’s agri-food sector.

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