International Labour Organization (ILO) Director-General Gilbert F. Houngbo has called for a human-centred approach to AI at the opening of the 114th International Labour Conference in Geneva. He said the future of work would depend not only on technological advances, but also on the policies, institutions and social dialogue shaping their impact on people’s lives.
Drawing on his report ‘A Moment of Choice: Harnessing Artificial Intelligence for Decent Work‘, Houngbo outlined an agenda focused on rights, employment and skills, social protection, and social dialogue. He argued that productivity gains generated by AI should be shared through higher wages, stronger labour protections and more inclusive economic growth.
Houngbo warned that decisions taken today would determine whether AI expands opportunity and shared prosperity or contributes to greater inequality and insecurity. He also situated AI governance within a broader context of economic uncertainty, citing ILO estimates that a prolonged oil-price shock could reduce global working hours by the equivalent of millions of full-time jobs and lead to significant labour income losses by 2027.
Delegates will also hold a second discussion on decent work in the platform economy, with the aim of developing new international labour standards. The draft Convention and Recommendation cover employment promotion, protections for digital platform workers, and provisions relating to automated systems used by digital labour platforms.
Delegates from governments, employers, and workers will also address gender equality, social dialogue, tripartism, and the application of labour standards. The conference, which brings together representatives from the ILO’s 187 Member States, will run until 12 June.
Why does it matter?
As AI becomes increasingly integrated into workplaces, governments, employers and workers are debating how productivity gains, skills requirements and labour protections should evolve. The ILO’s focus on human-centred AI reflects growing international efforts to ensure that technological change supports decent work rather than exacerbating inequality.
The discussions are also significant because they could influence future international labour standards for platform work and the use of automated systems in employment, helping shape how AI affects workers worldwide.
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The World Health Organization (WHO) has published a discussion paper examining how AI could reshape evidence-informed health policymaking. The paper, titled ‘Artificial intelligence and evidence-informed policy – emerging challenges and opportunities’, examines how AI can affect the way health problems are defined, policy options are designed, and impact is assessed.
The paper was developed jointly by WHO’s Department of Data, Digital Health, Analytics and AI and its Department of Science for Health. It is intended for policy-makers, regulators, health managers, and AI developers, and organises its analysis around the policy cycle, from understanding problems to designing solutions and monitoring implementation.
According to the paper, AI can strengthen policy analysis through the use of larger datasets, continuous evidence synthesis and faster scenario modelling. The paper also identifies risks throughout the policy cycle, including data bias, excessive focus on measurable indicators, digital divides, cybersecurity vulnerabilities and the possibility that automated monitoring systems could gradually shift policy implementation away from its original objectives.
A recurring concern is what the paper describes as ‘epistemic injustice’, whereby AI systems may prioritise quantifiable and data-rich evidence while overlooking lived experience, local expertise, Indigenous knowledge and community-based perspectives. WHO says existing evidence-informed policymaking tools and AI governance frameworks already converge on transparency, participatory engagement, rights protection, and risk-based oversight.
WHO recommends conducting algorithmic impact assessments and technology readiness reviews before deploying AI systems in policymaking processes. Once systems are deployed, WHO recommends continuous evidence-review processes, human verification mechanisms and multidisciplinary oversight, emphasising that AI should support rather than replace human judgement in health policymaking.
Why does it matter?
AI is increasingly being used to analyse large datasets, model policy scenarios and support public-sector decision-making. As governments and international organisations explore these capabilities, questions about transparency, accountability, bias and human oversight are becoming more important.
WHO’s recommendations highlight the need to balance AI’s analytical potential with safeguards that protect human rights, ensure inclusive policymaking and maintain human responsibility for policy decisions.
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Greece’s Minister of Digital Governance and Artificial Intelligence, Dimitris Papastergiou, has outlined a broad digital transformation agenda in an interview with the newspaper Manifesto, highlighting new legislation, AI deployment, cybersecurity measures and digital public services.
Papastergiou highlighted the growing use of AI in public administration, including the mAigov digital assistant, which has handled more than 4.4 million citizen queries. Greece is also investing in AI infrastructure projects, including the Daedalus supercomputer and the Pharos AI Factory, while preparing national legislation aligned with the EU AI Act.
The minister also highlighted a memorandum of understanding with voice AI company ElevenLabs aimed at improving accessibility and public services through voice-based technologies. Additional initiatives include the creation of a Unified Property Hub, stronger anti-phishing measures, a National Malicious Websites Blocking List, the Defective Vehicle Recall Registry and enhancements to the MyStreet application.
Greece’s plans illustrate how governments are increasingly combining AI deployment, digital public services and cybersecurity measures within broader digital transformation strategies.
The initiatives also reflect wider European efforts to improve interoperability, strengthen digital infrastructure, enhance online safety for children and prepare for the implementation of the EU AI Act.
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The Computers, Privacy and Data Protection (CPDP) conference is an annual gathering that brings together academics, policymakers, industry representatives, civil society, students, and EU institutions to discuss emerging digital policy challenges. This year’s theme was ‘Competing Visions, Shared Futures’, the 19th in the series, and it hosted approximately 150 panels over the span of 3 days in Brussels.
What is CPDP?
CPDP’s value lies in its multidisciplinary approach. With academics presenting their work or debating topical issues, as well as with industry and policy experts bringing their expertise to the table, the event creates a space for honest conversations among participants.
The conference is sponsored by organisations such as Google, TikTok, Apple, as well as the European Data Protection Supervisor (EDPS), European Union Agency for Fundamental Rights (FRA) and VBU. Google even presented its Banana AI model in a photo booth, allowing participants to modify photos they took in the booth.
Alongside panels, CPDP hosts an array of workshops, short films, artwork, radio programming, promotion booths, dedicated DPO, youth, finance and IT tracks, book launches, and pop-up exhibitions. The event always closes the day in style with an open bar and a party to chat and network at.
CPDP is not a typical conference with just panels, attendees, moderators, and lengthy speeches. The conference inspires creativity and gives the freedom to achieve it. This was proven by the diverse topics showcased in the event’s schedule over the three days.
From a fireside chat with the artist, Simon Denny, behind the conference’s art, who uses AI as a medium in some of his work, to typical discussions about the Digital Omnibus or tracking period apps, all the way to an exiled journalist talking about Russian internet censorship. There was something for everyone.
Image via Magnific
What was presented?
The breadth of topics discussed at CPDP offers insight into the issues currently shaping Europe’s digital policy agenda. There were approximately 150 panels in total, with data protection, AI, the Digital Omnibus and the topics of digital sovereignty receiving the most attention. Data protection received the most attention overall, as 33 panels were dedicated to the topic. This was followed by 26 panels on AI, 12 on the Digital Omnibus, 10 on digital sovereignty, and 7 on child-related protection.
The distribution of panels reflects the growing prominence of AI in digital policy discussions. However, data protection topics, including privacy and the GDPR, are still the frontrunners in terms of topic relevance. Newer and emerging topics reveal what is topical in the digital world.
Growing concerns over US tech reliance have intensified discussions about EU digital sovereignty. Alongside this, another heavily debated and sensitive topic is child protection in the online context and its generative AI implications, which raises questions about how to better protect children online.
Emerging topics at CPDP
Digital sovereignty is a challenging topic as it encompasses a lot and has yet to be defined, meaning that taking action can look different for a wide variety of actors. Several discussions framed digital sovereignty as a pathway towards greater digital independence and reduced reliance on external technology providers. In order to try to achieve digital sovereignty, public procurement should be steered away from non-EU actors and towards EU businesses to develop a European stack.
Yes, private partnerships are important, but public ones set the tone. Several participants argued that public procurement choices will play an important role in determining whether EU can strengthen domestic digital capabilities and reduce strategic dependencies. Digital sovereignty needs to come from all corners of the market and society; that is the challenge.
A very interesting panel on data protection and AI, the GDPR, and privacy occurred. In Academic Session I, Stephanie von Maltzan presented findings about her groundbreaking research on LLM unlearning. The larger the LLM, the more data points it will be trained on and the more complex its ‘web’ will be.
Removing data points is not a common practice, given how data points interact with each other, meaning that complexity overrides certain fundamental rights. For example, when data subjects invoke their right to erasure under Article 17 of the GDPR, they may request that certain data be deleted in an LLM, yet this request is difficult to carry out in practice.
The research highlights one of the emerging challenges at the intersection of AI governance and data protection. She presents a two tier model in which the actively deployed LLM is accompanied by a parallel ‘shadow’ model.
After receiving a valied erasure request, the ‘shadow model’ would undergo the necessary unlearning processes to remove the relevant data. In the second tier, in a scheduled update, the ‘shadow’ model, which had undergone unlearning, would replace the initial LLM, thereby upholding data subject requests.
Apart from these insightful exchanges of knowledge on AI, digital sovereignty and data protection, the conference offered practical workshops on how to brainstorm re-writing the proposed Article 88b of the Omnibus, data protection officer and cybersecurity crisis scenarios, as well as open conversations about how to protect children in online environments.
Remaining questions
The conference also highlighted several unresolved policy questions that continue to shape European digital governance debates.
Regarding the Digital Omnibus, would companies scale up overnight if we removed regulations?
Does digital sovereignty need/have a definition, or should it be left to the meaning of ‘digital independence’?
Open markets vs data protection, where is the balance?
Regarding digital sovereignty, which clouds should be used in the EU?
Should simplification mean using the once-used definition of personal data by the CJEU, or sticking to the definition relied on in law, cases, and practice?
In order to protect EU sovereignty, should parts of the stack be a public utility?
Why does it matter?
CPDP 2026 demonstrated that while privacy and data protection remain central pillars of European digital policy, debates around AI governance, digital sovereignty and online child protection are rapidly gaining prominence.
The discussions highlighted the growing challenge of balancing innovation, competitiveness, fundamental rights and strategic autonomy as Europe defines its digital future.
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Microsoft has introduced Majorana 2, its next-generation topological quantum chip, alongside the general availability of Microsoft Discovery, an AI-powered research platform designed to accelerate scientific discovery.
The company says the new chip delivers a 1,000-fold improvement in qubit reliability compared with the previous generation, representing a step towards more scalable quantum computing.
Majorana 2 incorporates a new materials stack based on lead superconductors, enabling a mean qubit lifetime of 20 seconds, with some qubits remaining stable for up to 1 minute. Microsoft says the improvement has allowed it to shorten its projected timeline for a scalable quantum computer, aiming for 2029.
A key element of the announcement is the role of Microsoft Discovery, the company’s agentic AI platform for scientific research and development. Microsoft said its quantum team used specialised AI agents to automate measurements, optimise fabrication processes, analyse large datasets, identify previously unnoticed flaws, and generate new research hypotheses.
According to Microsoft, agentic AI has become a regular part of its quantum research workflow, supporting scientists and engineers as they manage complex materials, fabrication, software, and measurement challenges.
The company also announced that Microsoft Discovery is now generally available for organisations conducting research in sectors such as life sciences, materials science, chemicals, energy, manufacturing, and consumer goods. A free local application is also being released in preview, allowing individual researchers to access core AI-driven research capabilities through a GitHub Copilot account.
Why does it matter?
Quantum computing still faces major barriers around qubit stability, reliability, error correction, and scalability. Microsoft’s announcement is significant because it links progress in quantum hardware with the use of agentic AI in scientific workflows. If the company’s roadmap holds, AI-assisted research could help accelerate progress towards practical quantum systems, with potential long-term implications for materials science, energy, health, chemistry, and other fields that depend on complex simulation.
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Singapore’s Personal Data Protection Commission (PDPC) has launched a public consultation on proposed advisory guidelines governing the use of personal data in generative AI systems. Published on 2 June, the draft guidelines seek feedback on how Singapore’s Personal Data Protection Act (PDPA) applies when personal data is used in the development and deployment of generative AI systems.
The proposed guidelines address the collection and use of personal data for generative AI model development, the allocation of data protection responsibilities across the AI lifecycle, and the handling of individual rights requests relating to personal data. The guidance is organised around development, deployment, and post-deployment stages.
For model development, the draft guidelines clarify how organisations may rely on exemptions for publicly available information when using web-scraped datasets containing personal data. They also set out considerations for data behind digital barriers such as paywalls, registration requirements, authentication mechanisms, and tools that block automated access.
The PDPC proposes that general privacy notices should not be considered sufficient for obtaining consent to use personal data for large-scale AI training or fine-tuning. Organisations would instead be expected to provide AI-specific notices explaining the categories of personal data used, the purpose of the processing, the model’s intended functions, and how individuals can refuse or withdraw consent.
The proposed guidelines also outline responsibilities for model providers, system providers, and system deployers, including retention, protection, purpose limitation, and accountability obligations. The post-deployment guidance addresses access and correction requests while recognising technical challenges associated with large datasets, embeddings, temporary context windows and the removal of specific information from trained models. Interested parties may submit comments to the PDPC by 1 July 2026.
Why does it matter?
The consultation highlights the growing challenge of applying existing data protection laws to generative AI systems that rely on large-scale data collection and model training. Regulators worldwide are increasingly examining how privacy principles such as consent, transparency and purpose limitation should operate in AI development.
Singapore’s proposed guidance could provide an important reference point for organisations developing or deploying generative AI, particularly in areas such as web scraping, AI training datasets and the allocation of responsibilities across the AI value chain.
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NGI Commons has outlined expectations for the European Union’s forthcoming Tech Sovereignty Package, a policy initiative aimed at strengthening Europe’s control over critical digital technologies and reducing reliance on non-European providers.
The initiative is expected to focus on semiconductors, cloud computing, AI and open-source software. According to NGI Commons, the package aims to align and simplify existing policies rather than introduce a new layer of regulation.
The framework builds on recommendations from Mario Draghi’s report on European competitiveness and seeks to support innovation, competitiveness and the EU’s broader objective of open strategic autonomy. A central element of the proposal is the recognition of open technologies as digital commons that underpin Europe’s digital ecosystem.
The analysis argues that open-source software should be treated as strategic infrastructure and supported through long-term funding, coordinated development efforts and greater public-sector adoption to strengthen digital resilience and security.
The report notes that challenges remain, including securing long-term funding, managing the growing energy demands of AI infrastructure and attracting investment, as policymakers seek to balance technological sovereignty with competitiveness.
Why does it matter?
The Tech Sovereignty Package is expected to shape how Europe approaches critical technologies such as semiconductors, cloud services, AI and open-source software in the coming years.
By treating open technologies as strategic infrastructure, policymakers could strengthen digital resilience, reduce external dependencies and support the EU’s broader goal of technological sovereignty while maintaining competitiveness in the global digital economy.
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NVIDIA has announced a major expansion of its AI Cloud ecosystem, supporting the rapid global deployment of AI factory infrastructure designed to meet growing demand for agentic AI, physical AI, sovereign AI and large-scale inference workloads.
The initiative aims to expand access to high-performance computing resources for enterprises, startups, governments, researchers and AI developers worldwide.
According to NVIDIA, the ecosystem now spans six continents, with new partners expanding AI Cloud infrastructure across multiple regions. The company said the expansion is intended to bring AI computing resources closer to users, industries and national AI initiatives while supporting regional and sovereign AI requirements.
Several cloud providers are expanding infrastructure to support advanced AI applications, including model training, fine-tuning, inference and AI agent development. Companies including CoreWeave, Firmus, Nebius and others are deploying new AI factories capable of supporting model training, fine-tuning, inference and AI agent development.
NVIDIA also highlighted growing adoption of its DSX platform, which is designed to help cloud providers deploy and manage AI factories more efficiently. The company said AI infrastructure is increasingly being assessed using metrics such as cost per token, energy efficiency and infrastructure utilisation, rather than raw computing capacity alone.
Why does it matter?
The expansion highlights the growing importance of AI infrastructure as governments and companies compete to secure the computing resources needed for advanced AI systems. Access to large-scale computing capacity is increasingly viewed as a strategic asset, particularly as countries pursue sovereign AI initiatives and seek greater control over critical digital infrastructure.
The announcement also reflects a broader shift in the AI industry, where demand is expanding beyond model training to include inference, autonomous agents and robotics applications, placing new emphasis on infrastructure efficiency, energy use and geographic distribution of computing resources.
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The UK Government has announced an amendment to Ofcom’s Illegal Content Codes of Practice under the Online Safety Act, introducing new measures to tackle non-consensual intimate images. The update was outlined in by the Minister for AI and Online Safety, Kanishka Narayan.
The amendment requires relevant online services to use perceptual hash-matching technologies, or equivalent tools, to identify and prevent the re-upload of known non-consensual intimate images, including AI-generated intimate image deepfakes.
According to the government, the change strengthens the framework established by Ofcom’s Illegal Content Codes of Practice, which entered into force in 2025. The updated approach aims to ensure that once abusive content has been identified and removed, systems are in place to prevent it from being repeatedly shared.
The amendment has been laid before Parliament for scrutiny and will take effect if neither House objects. The government said the measure is intended to strengthen protections for victims, particularly women and girls, and forms part of the ongoing implementation of the Online Safety Act in the UK.
Why does it matter?
Governments and regulators are increasingly treating AI-generated intimate imagery as a form of image-based abuse alongside authentic non-consensual intimate content. As generative AI tools make it easier to create and distribute realistic deepfakes, policymakers are looking for mechanisms to prevent harmful content from repeatedly reappearing online.
The UK’s proposal reflects a broader trend towards requiring platforms to deploy technical measures that can identify and block known abusive content while strengthening protections for victims of online harms.
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A gLocal Evaluation Week 2026 session will examine how to measure the impact of AI on learning outcomes for children and adolescents.
The event, titled ‘Measuring AI impact on learning outcomes’, is scheduled for 2 June and will focus on evidence gaps around AI use in education, particularly in Latin America and the Caribbean.
The session will discuss how AI is entering classrooms through personalised learning, tutoring, and teaching tools. UNICEF says some applications show promising results, but many AI tools used across the region lack rigorous impact-focused evaluation measures needed to assess whether they improve learning outcomes and can be scaled effectively.
The discussion will bring together government, research, and evaluation experts to assess existing evidence, identify promising results, and examine gaps in measuring AI’s contribution to learning outcomes at scale.
Participants will also consider unintended effects, including bias and the exclusion of marginalised groups. UNICEF says policymakers still face uncertainty over what works, for whom, and under what conditions when deciding whether to invest in AI tools for education.
Speakers listed for the session include Fiorella Haim of Ceibal, Martín Elías De Simone of the World Bank, Juliette Norrmen-Smith of UNICEF’s Office of Innovation, María Paz Monge of J-PAL-LAC, and Michael Craft of UNICEF. The event will include simultaneous Spanish-English interpretation.
Why does it matter?
The session highlights a key challenge in AI and education: adoption is moving faster than evidence. As AI tools enter classrooms, policymakers need stronger evaluation methods to determine whether they improve learning outcomes, work for different groups of children, and risk reinforcing bias or exclusion.
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