Australia’s eSafety Commissioner has published two studies showing that parents are helping children develop safe digital habits from an early age, while highlighting growing gaps in awareness of newer online risks such as generative AI, algorithmic recommendations and online sexual extortion.
The research found that Australian parents actively supervise their children’s online activities, establish rules for internet use and regularly discuss online safety.
Among children aged three to ten, almost all had used the internet outside school or childcare settings, while nearly nine in ten parents had discussed online safety and around half allowed internet use only under parental supervision. However, significantly fewer parents had spoken about algorithmic recommendations or exposure to sexual content online.
The second report, covering children aged 10 to 17, found similarly high levels of parental engagement, with most parents regularly discussing online safety, setting internet rules and providing ongoing guidance. However, conversations about emerging risks were far less common: only 26% had discussed how algorithms shape online content, 11% had spoken about generative AI and just 8% had addressed online sexual extortion.
The eSafety Commissioner said digital parenting should evolve alongside children’s online experiences and stressed that responsibility should not rest solely with parents.
The findings suggest that while many parents have successfully integrated online safety into everyday family life, digital literacy is struggling to keep pace with rapidly evolving technologies. Emerging issues such as generative AI, algorithm-driven content and online exploitation require new forms of guidance that extend beyond traditional internet safety advice.
The research also reinforces the idea that protecting children online is a shared responsibility. Alongside parents, governments, schools and technology companies all have a role in ensuring that children develop the knowledge and skills needed to navigate an increasingly complex digital environment.
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The Associated Press (AP) has updated its AI newsroom guidelines, expanding the ways journalists may use generative AI while reaffirming that human oversight, verification and editorial accountability remain central to its reporting standards.
Approved uses include early-stage research, document summarisation, transcription, translation and assistance with headlines, story summaries and shot lists. AI may also support grammar, spelling and search optimisation.
However, all AI-generated outputs must be reviewed and edited by an AP journalist before publication. The technology cannot replace original reporting, source verification, fact-checking or editorial judgement.
The updated guidelines continue to prohibit the use of generative AI to create, alter or enhance news photography, maintaining that visual journalism must preserve the authenticity and evidentiary value expected of news images.
The guidance also establishes new standards for reporting on manipulated AI-generated content, requiring journalists to verify such material, identify it clearly and explain its context.
Disclosure will also be required whenever generative AI plays a material role in published content. In addition, the policy introduces standards governing the use of AI coding assistants by software developers working on newsroom systems.
AP said accuracy, fairness and speed remain the guiding principles of its journalism and that the newsroom’s AI policies will continue to evolve as the technology and associated risks develop.
Why does it matter?
As news organisations increasingly adopt generative AI, clear editorial governance is becoming as important as the technology itself. Policies that require human verification, transparency and editorial responsibility help ensure that AI supports journalism without undermining public trust in news reporting.
The AP’s updated guidelines also reflect a broader industry shift towards defining practical rules for AI use rather than treating the technology as either entirely acceptable or entirely prohibited. Such frameworks are likely to influence how other media organisations balance innovation with accuracy, accountability and transparency.
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Microsoft has developed a research tool that allows communities to help shape how they are represented in AI-generated images, addressing long-standing concerns that AI training data often reflects incomplete or biased online content rather than the perspectives of the people depicted.
Instead of relying solely on existing online datasets, communities, including people with disabilities and others with shared lived experiences, can work through advocacy organisations to create their own image and video libraries. Each image is accompanied by descriptions explaining what is important about the scene, giving AI systems contextual information based on the community’s own perspective rather than visual appearance alone.
Developed by Microsoft’s Accessibility Team, the process guides participants from selecting meaningful images to curating a library of around 400 real-world examples organised around shared themes such as family life and everyday routines. These annotated collections are then used to generate AI images, which community members review and evaluate against their own standards of accurate representation, creating a feedback loop intended to improve future outputs.
A central feature of the project is that advocacy organisations retain ownership of the resulting datasets and decide whether, how and with whom they are shared, including publication on platforms such as Hugging Face. Communities can also withdraw their data if consent changes over time.
Why does it matter?
The project addresses one of the central challenges in AI governance: who decides how people and communities are represented in training data. Many AI systems are built using large-scale datasets collected from the internet with limited transparency, consent or community involvement, increasing the risk of inaccurate, stereotypical or exclusionary outputs.
By giving communities ownership of their data and a direct role in defining what constitutes fair representation, Microsoft’s approach shifts part of the AI development process towards participatory governance. If adopted more widely, it could provide a model for building datasets that are not only more representative but also more transparent, accountable and consent-based.
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India is strengthening enforcement of its online child safety framework by introducing stricter obligations for social media platforms, including faster content removal requirements and new safeguards for AI-generated content.
The Ministry of Electronics and Information Technology (MeitY) said it has requested a detailed report from a social media platform following allegations that advertisements linked to child sexual abuse material (CSAM) appeared on its service. The National Commission for Protection of Child Rights has also issued notices to the platforms concerned.
The updated framework significantly shortens compliance deadlines for intermediaries. Platforms must remove unlawful content within three hours of receiving a court order or a reasoned government notice, compared with the previous 36-hour deadline.
Complaints involving nudity, morphed intimate images and similar sensitive content must be addressed within two hours, while intermediaries are also required to report offences involving CSAM and other relevant crimes to the appropriate authorities.
Significant social media intermediaries must also deploy automated tools and other technical measures to proactively detect CSAM and previously identified illegal content. India said compliance will be reinforced through government advisories and a standard operating procedure on non-consensual intimate imagery issued in 2025.
Authorities warned that platforms failing to meet their due diligence obligations could lose the liability protections provided under Section 79 of the Information Technology Act and face prosecution under applicable laws.
Why does it matter?
India’s measures reflect a broader shift towards faster and more proactive platform accountability. Rather than relying primarily on user reports, regulators are increasingly requiring platforms to respond within hours, deploy automated detection systems and demonstrate that they can effectively prevent the spread of harmful content.
The inclusion of specific obligations for AI-generated content also illustrates how online safety regulation is evolving to address emerging risks such as deepfakes and synthetic child exploitation material. Together, the measures reinforce the expectation that platforms are responsible not only for removing illegal content but also for preventing its creation, distribution and recurrence.
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India is expanding its BHASHINI language AI platform to Goa, where it will support digital public services in Konkani and other Indian languages as part of the country’s broader multilingual digital public infrastructure strategy.
BHASHINI provides AI-powered translation, speech recognition, text-to-speech and transliteration, enabling government platforms and digital services to operate across multiple Indian languages. The platform supports applications in healthcare, education, tourism and citizen services, helping make public services more accessible across linguistic communities.
Discussions in Goa also focused on expanding Konkani language datasets to improve AI model performance and support the development of locally relevant digital applications. The initiative encourages collaboration between government agencies, researchers and technology developers to strengthen language resources for AI.
BHASHINI already supports dozens of Indian and international languages and has been integrated into hundreds of government websites, reflecting India’s broader strategy of using multilingual AI to strengthen digital public infrastructure and improve access to government services.
Why does it matter?
The expansion of BHASHINI illustrates how India is using AI to make digital public infrastructure more inclusive in a linguistically diverse country. By enabling government services to operate across multiple languages, the platform can help reduce language barriers that often limit access to healthcare, education and other public services.
The initiative also highlights the growing importance of language data as public digital infrastructure. Improving datasets for regional languages such as Konkani not only enhances AI performance but also helps ensure that smaller linguistic communities are represented as AI technologies become more widely integrated into public administration.
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For much of the past decade, discussions on AI governance have focused on algorithms. Policymakers, regulators, and researchers have debated ethics, transparency, bias, accountability, and human oversight, seeking to ensure that AI systems remain trustworthy and aligned with fundamental rights.
These questions remain essential, but they are no longer sufficient to explain the direction of AI policy.
A new reality is emerging. Increasingly, governments are recognising that the ability to govern AI depends not only on regulating algorithms but also on securing the infrastructure that makes them possible. Data centres, advanced semiconductors, cloud computing, electricity grids, submarine cables, digital public infrastructure, and skilled workforces have become as strategically important as the AI models themselves.
The shift has been gradual rather than abrupt, yet it is now evident across national strategies, international organisations, and multilateral discussions. AI governance is expanding beyond software governance to become infrastructure governance.
AI runs on infrastructure, not algorithms alone
The rapid adoption of generative AI has often obscured a simple reality that every AI system rests on a vast physical and institutional foundation.
Large language models require enormous computing power, specialised chips, cloud infrastructure, reliable electricity, high-speed connectivity, trusted datasets, secure digital environments, and engineers capable of developing and maintaining increasingly complex systems. None of these components can be built overnight, and few can be developed without substantial public and private investment.
Image via Magnific
As a result, AI capability is increasingly determined by access to infrastructure, not just software.
Countries may have ambitious AI strategies, talented researchers, or innovative start-ups, but without advanced computing capacity, modern data centres, resilient cloud infrastructure, and skilled human capital, those ambitions become difficult to realise. Governments are therefore beginning to treat AI infrastructure as a strategic national asset rather than simply a commercial resource.
Industrial policy is becoming a pillar of AI policy
This shift is increasingly reflected in public policy.
The European Union has complemented its landmark AI Act with broader industrial initiatives, including the European Chips Act, the AI Factories initiative under EuroHPC, and, most recently, plans to develop AI Gigafactories capable of supporting the next generation of AI models. The recently adopted Digital Omnibus on AI also strengthens the role of the European Commission’s AI Office while simplifying implementation aspects, illustrating that regulation, institutional capacity, and infrastructure investment are evolving together.
The United States has pursued a different approach, combining export controls on advanced semiconductors with large-scale investment in domestic chip manufacturing and AI infrastructure. China continues to expand its national computing centres, cloud capacity, and state-backed AI ecosystems as part of its long-term industrial strategy.
Although these approaches differ politically and economically, they increasingly share the view that governing AI requires building the infrastructure that enables it.
AI policy, in other words, is beginning to resemble industrial policy.
Infrastructure has become a development issue
The infrastructure shift is equally visible in developing countries, where the conversation is moving beyond access to AI applications towards the ability to build AI capacity locally.
Throughout the World Summit on the Information Society (WSIS) Forum 2026, discussions repeatedly highlighted that meaningful digital transformation depends on foundational infrastructure. African policymakers, researchers, and technical experts argued that the continent suffers not primarily from a shortage of digital ambition, but from limited access to computing capacity.
Speakers pointed out that Africa has only a tiny share of global AI compute and data centre capacity, forcing many researchers and innovators to rely on infrastructure located elsewhere. The challenge, they argued, is no longer simply connectivity but compute sovereignty, the ability to build, train, and deploy AI systems locally.
The same message emerged during discussions on digital sovereignty. Rather than focusing solely on access to foreign AI models, participants argued that exporting raw data while importing AI services mirrors older economic patterns in which countries exported raw materials while importing higher-value finished products. Building local AI capability, therefore, requires investment in data centres, energy systems, cloud infrastructure, skills development, and trusted data ecosystems.
These discussions suggest that AI infrastructure is becoming an increasingly important component of development policy.
Infrastructure is also becoming geopolitics
The growing strategic importance of AI infrastructure extends far beyond economic development.
Competition over semiconductor manufacturing, cloud services, critical minerals, advanced computing facilities, and energy has become a defining feature of international relations. Governments increasingly view these assets through the lens of economic security, technological resilience, and geopolitical influence.
Submarine cables illustrate this evolution particularly well. Once regarded primarily as telecommunications infrastructure, they are now recognised as essential to AI systems that depend on massive volumes of cross-border data traffic. Recent international efforts to strengthen submarine cable resilience reflect how infrastructure once considered largely invisible has become central to digital governance.
Image via Magnific
Electricity is undergoing a similar transformation. As AI models become increasingly computationally intensive, access to affordable and reliable energy is becoming a key factor determining where data centres can be built and where AI innovation can flourish. This has encouraged governments to align AI strategies with broader industrial, energy, and climate policies.
In this environment, AI governance increasingly overlaps with trade policy, investment screening, industrial strategy, critical infrastructure protection, and national security.
A broader understanding of AI governance
This evolution does not diminish the importance of ethical AI principles or risk-based regulation. Questions surrounding transparency, accountability, bias, safety, and human rights remain fundamental.
Rather, it broadens the understanding of what AI governance entails.
Traditional AI governance focuses on how AI systems should be designed, deployed, and used responsibly. Infrastructure governance raises a different set of questions, such as who has access to the computing power needed to develop advanced AI? Who controls the cloud infrastructure that underpins AI services? Where are critical datasets stored? Which countries have the physical, financial, and institutional capacity to participate meaningfully in the AI economy?
image via Magnific
These questions ultimately shape who can innovate, compete, and benefit from AI.
As a result, debates that once focused primarily on algorithms increasingly encompass semiconductors, cloud infrastructure, digital public infrastructure, data centres, energy systems, connectivity, and workforce development.
From governing AI to enabling AI
The next phase of AI governance is likely to be defined less by entirely new regulatory principles than by long-term investment decisions.
Building trustworthy AI ecosystems will require governments to develop resilient digital infrastructure, strengthen education and digital skills, encourage research, modernise energy systems, expand computing capacity, and foster international cooperation on shared digital resources.
The countries that succeed may not simply be those that write the most comprehensive AI regulations. They may instead be those that create the conditions that allow responsible AI to flourish in the first place.
Image via Magnific
The global conversation about AI has not moved beyond governance. It has moved deeper into its foundations.
In this sense, the future of AI governance may increasingly depend not only on how societies regulate AI, but also on how they build the infrastructure that makes AI possible.
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Australia will retain its existing AI copyright rules, rejecting calls for a text and data mining exemption as the government seeks to attract AI investment without weakening protections for creators.
Industry and Innovation Minister Tim Ayres said existing copyright protections would remain in place while the government works towards a framework that provides greater certainty for both technology companies and rights holders. He said attracting AI investment should not come at the expense of creators’ rights.
The comments follow calls from AI companies for clearer access to copyrighted material for model training. Ayres acknowledged that technology firms and rights holders favour different approaches, while Attorney-General Michelle Rowland continues consultations aimed at reaching a compromise. Although no timetable has been announced, Ayres said the government wants to resolve the issue quickly without overriding creators’ control over their work.
Australia’s wider AI agenda also includes legislation establishing standards for large data centres, including requirements relating to electricity supply, grid stability and water security. Ayres said implementation would be coordinated by the Office of Artificial Intelligence, while the AI Safety Institute continues collaborating with international partners, security agencies and frontier AI developers.
Ayres described the reforms as a national economic and security priority, arguing that Australia should help shape AI rather than depend solely on technologies developed elsewhere. The government is also working with businesses and trade unions on workforce adaptation as AI transforms employment.
By maintaining its AI copyright rules while developing infrastructure and safety standards, Australia is seeking to balance investment, technological sovereignty, creators’ rights and public trust.
Why does it matter?
Australia’s approach illustrates the growing challenge governments face in balancing AI innovation with intellectual property protections. By rejecting a broad copyright exemption while continuing consultations, Canberra is signalling that attracting AI investment should not automatically come at the expense of creators’ rights.
The announcement also shows that AI governance is extending beyond model regulation to encompass copyright, infrastructure, energy security and workforce policy. Rather than treating these as separate issues, Australia is developing a broader national strategy that links AI competitiveness with economic resilience, public trust and technological sovereignty.
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The UK’s Department for Science, Innovation and Technology (DSIT) has urged businesses to make essential digital services more accessible, reliable and easier to use, arguing that better service design is key to reducing digital exclusion.
In an open letter to industry leaders, DSIT noted that digital platforms are now central to everyday activities such as banking, bill payments, transport and access to information. It invited businesses to work with government to improve the accessibility and usability of essential online services.
The department warned that digital exclusion extends beyond internet access and technical skills. Around 27% of UK adults are classified as ‘narrow internet users’, while 43% rely on someone else to complete online tasks, suggesting that many existing digital services remain difficult to use.
The initiative encourages organisations to simplify digital journeys, consider the needs of disabled users and people at risk of digital exclusion, and maintain strong standards for safety, privacy and security. DSIT is seeking industry support for shared standards, a cross-sector roadmap and practical improvements to service design.
Why does it matter?
The initiative reflects a growing recognition that digital inclusion depends not only on internet access, but also on whether online services are designed to be understandable, accessible and usable for diverse groups of people. Poorly designed digital services can create barriers even for those who are already connected.
By encouraging voluntary collaboration between government and industry, DSIT is also signalling a broader shift towards treating accessibility and user experience as essential elements of digital public policy alongside cybersecurity, privacy and digital infrastructure.
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The Spanish Ministry for Digital Transformation and Public Service has announced a €1.92 million investment in Kaleidos to accelerate development of its open-source interface design platform, Penpot, as part of a €6.9 million public and private funding round.
According to the Ministry, the investment is intended to strengthen European digital sovereignty by supporting an open-source alternative to dominant foreign interface design software. Penpot combines design and software development in a single platform while supporting collaborative workflows and generative AI applications.
Founded in 2011, Kaleidos originally developed Penpot as an internal innovation project. The company plans to use the funding to expand its commercial operations and paid services. It says the platform treats design as code, enabling integration with multiple AI tools rather than a single proprietary ecosystem.
The investment is being made through the Spanish Society for Technological Transformation’s Next Tech facility, supported by the EU Recovery, Transformation and Resilience Plan. According to the ministry, the initiative is intended to promote innovation, open-source technologies and Europe’s digital autonomy while reinforcing Spain’s role as the project’s base.
Why does it matter?
The investment illustrates how governments are increasingly using industrial policy to strengthen digital sovereignty by supporting open-source alternatives to dominant proprietary technologies. Rather than relying solely on regulation, European countries are also investing directly in technologies they see as strategically important for innovation and technological resilience.
The focus on an AI-compatible, open-source platform also reflects growing demand for tools that combine collaborative software development with flexible AI integration. Supporting interoperable platforms could help reduce vendor lock-in while encouraging a more diverse and competitive European digital ecosystem.
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The Internet Watch Foundation (IWF) has partnered with Swedish behavioural detection technology company Tuteliq to strengthen the early detection of online grooming and child sexual abuse material (CSAM).
As a new IWF Member, Tuteliq will integrate the organisation’s URL List and Image Hash List into its detection platform, enabling online services to identify and block known CSAM more effectively.
Unlike traditional moderation systems that analyse individual messages or files, Tuteliq’s technology examines how conversations evolve over time to identify behavioural patterns associated with grooming, coercion and online sexual exploitation before abuse occurs.
The platform analyses text, voice, images and video across 27 languages and is intended for youth-focused apps, gaming platforms, sports organisations and other online communities.
The partnership combines Tuteliq’s behavioural analysis with the IWF’s verified databases of known CSAM, aiming to strengthen prevention alongside detection by enabling earlier intervention before harmful content is created or shared.
Beyond the technical integration, Tuteliq will also draw on the IWF’s research, policy expertise and threat intelligence to further develop its detection models. According to the organisations, the collaboration reflects a broader shift towards combining behavioural analysis with verified intelligence to improve online child protection.
Why does it matter?
The partnership reflects a broader shift from reactive content moderation towards preventing online abuse before illegal material is created or shared. Behavioural AI capable of identifying grooming patterns could allow platforms to intervene earlier, potentially reducing harm before exploitation escalates.
At the same time, expanding behavioural detection raises important questions about transparency, privacy and accountability. As platforms increasingly analyse patterns of user behaviour rather than individual pieces of content, ensuring appropriate safeguards and oversight will become an important part of child online safety governance.
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