The alliance said age restrictions on mainstream social media platforms could reduce some risks. Still, children may move to less regulated digital spaces, including encrypted messaging services, gaming platforms and other online environments where grooming, sexual extortion and abuse can continue.
UK ATOC called for a broader, system-wide response focused on prevention, stronger platform accountability and safer-by-design digital services. It said governments, regulators, technology companies and online service providers share responsibility for reducing opportunities for abuse before harm occurs.
The alliance proposed a package of technical, legislative and regulatory measures. These include stronger safeguards in end-to-end encrypted environments, robust age-assurance systems, mandatory safer-by-design principles, stronger enforcement under the Online Safety Act and clearer regulation of AI chatbots and companion services.
It also called for device-level nudity detection, upload prevention for known child sexual abuse material and measures to address livestreamed abuse, grooming and sexual extortion.
UK ATOC welcomed the government’s plan to introduce nudity-detection tools on children’s devices, describing it as an important additional safeguard.
The UK debate shows the limits of age-based social media bans as a child-safety tool. Online child sexual exploitation and abuse can move across platforms, devices, encrypted services, gaming environments and AI-enabled systems. UK ATOC’s response therefore shifts the focus from access restrictions alone towards prevention, safer design, platform duties and technical safeguards that address how abuse actually happens across digital services.
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Singapore’s Cyber Security Agency (CSA) has outlined new and ongoing initiatives to strengthen national cyber resilience as AI reshapes the cyber threat landscape.
The measures are detailed in the Singapore Cyber Landscape 2025/2026 report, which reviews cybersecurity trends and the country’s response to evolving digital threats.
CSA said AI is reshaping the global cyber threat environment by enabling attackers to operate with greater speed, scale and sophistication. The agency said agentic AI is a particular concern because autonomous systems could automate parts of the cyber kill chain, compressing attacks that once unfolded over days into hours.
The agency cited Anthropic’s Mythos and the misuse of OpenClaw, an open-source agentic AI framework, as examples of how AI can accelerate vulnerability research, exploit development and cyberattack preparation.
At the same time, CSA said AI can strengthen cyber defence by improving threat detection, accelerating incident response and helping organisations identify vulnerabilities more quickly. As AI systems become more widely deployed across enterprise networks and critical infrastructure, however, they are also becoming attractive targets, making secure AI deployment an increasing priority.
To support secure AI adoption, CSA has published Guidelines on Securing AI Systems and a Companion Guide for system owners. It also released a discussion paper on securing agentic AI systems in October 2025 and said it will continue working with international partners on AI security standards.
The report also highlights how AI is changing the tactics of phishing and scam operations. CSA said attackers can use AI to generate convincing phishing lures at scale, produce realistic voice clones and video deepfakes, and create tools that can bypass multi-factor authentication.
CSA also warned that AI is making phishing and scam campaigns more convincing through voice cloning, video deepfakes and large-scale generation of personalised phishing messages. Despite these growing capabilities, reported phishing cases fell by 21% in 2025 to around 4,800 incidents.
Singapore has also launched the pilot National Simulated Scams Exercise, supported by the Ministry of Home Affairs. The exercise simulated AI-enabled government official impersonation scam calls to help the public recognise and respond to emerging scam tactics.
CSA said the number of infected infrastructure units detected in Singapore rose sharply to 284,300 in 2025, a 142% increase from 2024. The increase was driven mainly by persistent malicious infrastructure activity and improved detection of infected botnet devices.
The agency said weakly secured consumer Internet-of-Things devices and unpatched firmware continue to create opportunities for botnet operators. To address this, all residential routers sold in Singapore must meet Cybersecurity Labelling Scheme Level 2 requirements by the end of 2027.
Ransomware also remained a significant threat, with reported cases rising slightly from 159 in 2024 to 165 in 2025. CSA said small- and medium-sized enterprises remained disproportionately affected due to lower cybersecurity maturity and limited resources.
To support SMEs, CSA backed the Cyber Resilience Centre, which provides cybersecurity health checks and recovery assistance after incidents. Eligible SMEs can also receive co-funding for cybersecurity advisory services through the CISO-as-a-Service programme.
One of the year’s most significant incidents involved an attempted intrusion by the APT group UNC3886 targeting Singapore’s four largest telecommunications operators. CSA said the attack was contained through Operation CYBER GUARDIAN without disruption to services or evidence of customer data being compromised.
CSA is also requiring critical information infrastructure owners to attain Cyber Trust mark certification by the end of 2027. The requirement is intended to extend good cybersecurity practices across broader enterprise environments that support critical infrastructure operations.
In 2025, Singapore also conducted its largest Exercise Cyber Star, involving close to 500 participants from CSA, the Singapore Armed Forces’ Digital and Intelligence Service and critical infrastructure owners across 11 sectors.
CSA said it has expanded Cyber Essentials and Cyber Trust mark certifications to include mandatory cloud and AI security requirements. More than 800 organisations had attained at least one Cyber Essentials certification as of early 2026.
The agency is also advancing Singapore’s National Quantum-Safe initiative, working with industry, academia and international partners to raise awareness of quantum risks, support migration planning and accelerate adoption of quantum-safe technologies.
CSA said Singapore will continue investing in cybersecurity capabilities, strengthening partnerships and supporting secure adoption of emerging technologies in an AI-driven threat landscape.
Commissioner of Cybersecurity and CSA Chief Executive David Koh said Singapore must ‘lock down, find first, and fix fast’ as AI and quantum technologies reshape cyber risks. He said the response must be continuous, with government, industry and citizens working together to ensure digital innovation develops alongside trust and security.
The report illustrates how Singapore is treating cybersecurity as a continuous national resilience effort encompassing AI, critical infrastructure, ransomware, online scams and future quantum threats.
Why does it matter?
Singapore’s strategy reflects a growing shift from reactive cybersecurity towards continuous cyber resilience. Rather than addressing individual threats in isolation, the government is integrating AI security, critical infrastructure protection, scam prevention, cybersecurity certification and quantum readiness into a coordinated national strategy.
The report also illustrates how AI is changing cybersecurity on both sides of the equation. While attackers are using AI to accelerate phishing, malware development and vulnerability exploitation, governments are increasingly deploying AI to strengthen cyber defence, making secure AI deployment and governance central components of national cybersecurity policy.
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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.
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 Sciencedemonstrated 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.
image via Magnific
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.
image via Magnific
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.
image via Magnific
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.
image via Magnific
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.
image via Magnific
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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The European Patent Office (EPO) has published its Annual Review 2025, revealing that European patent applications exceeded 200,000 for the first time in the organisation’s history.
The milestone reflects growing confidence in the European patent system, supported by continued investment in digital transformation, AI and more efficient patent examination processes under the Strategic Plan 2028.
The Office processed a record 418,868 patent dossiers during 2025, increasing productivity by 4% while maintaining high quality standards and improving the speed of patent searches, grants and opposition proceedings.
User satisfaction also remained high following the EPO’s largest-ever satisfaction survey, involving more than 8,000 participants. Innovation activity continued to grow across strategic sectors including digital technologies, healthcare, advanced materials and battery technologies.
AI played an increasingly important role throughout the patent granting process. The EPO expanded AI-powered tools for patent examiners, including a large language model-based enhancement to its PreSearch system, designed to improve prior art discovery while ensuring examiners retain full control over decision-making.
Additional AI-supported capabilities now assist with document analysis, advanced searches, file allocation and oral proceedings. At the same time, MyEPO continued evolving as the organisation’s central digital platform, while Online Filing 2.0 became the standard filing tool ahead of broader DOCX filing deployment.
The report also highlights the growing success of the Unitary Patent system, with SMEs, universities and public research organisations accounting for nearly half of all Unitary Patents granted to European innovators.
Alongside new innovation intelligence tools such as the Patent Standards Explorer, Digital Library and expanded Deep Tech Finder, the EPO says it is strengthening Europe’s innovation ecosystem through greater transparency, digital services and data-driven patent intelligence.
Why does it matter?
The Annual Review demonstrates how AI is becoming embedded within one of Europe’s most important innovation institutions. Rather than replacing patent examiners, AI is being deployed to improve search quality, accelerate administrative processes and strengthen decision-making while maintaining human oversight.
It also illustrates Europe’s broader strategy of combining AI adoption with digital public services, intellectual property protection and innovation policy.
Record patent demand, expanding use of the Unitary Patent and new digital tools suggest the EPO is positioning itself as a key pillar of Europe’s competitiveness in emerging technologies, particularly as global competition intensifies in AI, semiconductors, advanced manufacturing and deep tech.
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The academies were announced during Digital Skills EU Days, an annual event bringing together digital skills projects, national coalitions, policymakers, industry representatives and education organisations from across the EU.
Funded under the Digital Europe Programme, the academies are intended to establish specialised training in critical technology areas and help the EU meet its Digital Decade targets.
The Commission said Europe’s competitiveness and leadership depend on digital talent, linking the initiative to the Union of Skills, the AI Continent Action Plan, the Apply AI Strategy and the Digital Decade Policy Programme.
The new academies add to wider Digital Europe Programme investments in skilling, upskilling and reskilling. The programme has invested more than €294 million in the EU digital skills initiatives covering areas such as data, cloud, cybersecurity and AI.
During the event, the Commission also presented the 2026 European Digital Skills Awards, recognising projects focused on AI literacy, cybersecurity education, digital inclusion, research data management and women’s participation in ICT.
Why does it matter?
The new academies show that the EU is treating digital skills as part of its strategic technology agenda, alongside regulation, infrastructure and industrial policy. AI, quantum technologies and virtual worlds all require specialised expertise, and shortages in these areas could slow deployment across businesses, research institutions and public services. The initiative also supports the EU’s broader goal of strengthening technological competitiveness and reducing dependence on external talent and capabilities.
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South Korea and Japan have agreed to expand defence cooperation, including collaboration on AI and other advanced technologies, following talks between South Korean Defence Minister Ahn Gyu-Back and Japanese Defence Minister Shinjiro Koizumi in Seoul. The agreement was reached during a bilateral summit held in Seoul that day.
The ministers agreed to establish regular high-level visits and meetings, resume bilateral naval search and rescue exercises for the first time in nine years, and continue trilateral security cooperation with the United States to support regional peace and stability.
They also agreed to expand exchanges between South Korea’s Black Eagles and Japan’s Blue Impulse aerobatic teams to support search and rescue training. The agreement also included a commitment to strengthen ties in state-of-the-art science and technology, including AI, with the summit taking place at the Ministry of National Defence’s parade ground in Seoul.
Why does it matter?
The agreement marks a further improvement in defence relations between South Korea and Japan, whose security cooperation has often been constrained by historical and political tensions. The resumption of joint search and rescue exercises after nine years reflects growing alignment on shared regional security priorities.
The inclusion of AI and advanced technology cooperation also illustrates how emerging technologies are becoming integral to defence partnerships. As countries increasingly integrate AI into military planning, logistics and operational capabilities, technological collaboration is becoming a strategic component of broader security relationships, particularly within the Indo-Pacific.
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The Communications Security Establishment Canada (CSE) has published its 2025-2026 Annual Report, detailing the activities of the agency and the Canadian Centre for Cyber Security between April 2025 and March 2026 as cyber threats continued to grow in scale and complexity.
During the reporting period, the Canadian Centre for Cyber Security responded to more than 3,200 cybersecurity incidents affecting federal institutions and critical infrastructure. It also issued 25 alerts, 995 advisories and more than 97,000 notifications through the National Cyber Threat Notification System to 1,363 subscribed organisations.
CSE also took direct action against ten of the ransomware groups causing the greatest harm to Canada and its allies, while completing 1,772 supply chain risk assessments to strengthen cyber resilience across government. During the year, the agency received 13 ministerial authorisations, including four supporting foreign cyber operations.
The report highlights how recent defence investments are supporting work on secure digital infrastructure, stronger cyber defence capabilities, AI, post-quantum cryptography and deeper collaboration with trusted international partners.
Minister of National Defence David J. McGuinty said the report demonstrates the importance of CSE’s work to Canada’s security and economic well-being. Chief of CSE Caroline Xavier noted that the agency will mark its 80th anniversary in 2026 and said recent investments are providing the tools needed to address an increasingly complex threat environment.
Why does it matter?
The report illustrates how national cybersecurity agencies are shifting from responding to isolated incidents to maintaining continuous operations against increasingly sophisticated digital threats. Activities ranging from ransomware disruption to supply chain assessments demonstrate the expanding role of cyber defence in protecting governments and critical infrastructure.
The emphasis on AI, post-quantum cryptography and secure digital infrastructure also signals Canada’s long-term approach to cybersecurity. By investing in emerging technologies while strengthening cooperation with allies, CSE is preparing for a threat environment in which cyber resilience is closely tied to national security, economic stability and technological competitiveness.
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Workplace AI adoption in the UK has more than doubled over the past year, reaching 73%, according to a new Google report. However, the benefits remain uneven, with a small group of advanced users seeing significantly stronger career outcomes than the wider workforce.
The report categorises workers into four groups: AI Spectators, who have not yet engaged with the technology; Experimenters, who use basic AI functions; Practitioners, who use AI regularly; and AI Trailblazers, who apply it in advanced and innovative ways.
Although AI Trailblazers account for just 15% of users, they report significantly better outcomes, including faster promotions, larger pay increases and substantial weekly time savings.
The report found that advanced users outperform other workers across several indicators, including promotions, performance reviews and salary growth. However, differences in adoption across age, gender and geography suggest that unequal access to AI skills could widen existing labour market disparities.
Google argues that closing this gap will require greater investment in AI literacy, organisational support and workplace culture. Initiatives such as national upskilling programmes and diagnostic tools are intended to help workers progress from basic experimentation to more advanced AI use, supporting broader productivity growth.
Why does it matter?
The findings suggest that simply adopting AI is not enough to generate widespread economic benefits. The greatest productivity and career gains are concentrated among workers who integrate AI deeply into their daily work, highlighting the importance of skills development and organisational support.
The report also points to a growing policy challenge. If access to advanced AI skills continues to vary across demographic groups and regions, AI could widen existing inequalities in the labour market. Expanding AI literacy and helping more workers move beyond basic use may therefore be as important as increasing adoption itself.
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The multilingual course was launched in December 2025 by the Secretariat of the Global Alliance for Literacy, in collaboration with Huawei. It is available in Arabic, English, French and Spanish.
The programme focuses on practical digital skills that educators can apply in literacy classrooms. It also encourages participants to use digital tools responsibly, evaluate online information critically and understand how technologies, including AI, shape learning and information use.
The course is delivered through 11 self-paced sessions and encourages educators to reflect on their teaching practice while developing new skills.
Participants from countries including Mexico, Pakistan and Togo reported stronger confidence in using digital tools, more learner-centred teaching approaches and greater use of collaboration and assessment technologies.
UNESCO said national and municipal adult education agencies, adult learning providers and UNESCO Learning Cities are helping expand the course across countries.
Why does it matter?
Digital literacy is becoming essential for both educators and learners, especially as AI and online platforms reshape access to information. Training literacy educators first can create a multiplier effect, helping adult learners and underserved communities build practical digital skills, critical thinking and confidence in online environments. The programme also shows how international education initiatives are moving beyond access to focus on effective and responsible use of technology.
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South Korea’s National Assembly has approved a supplementary budget of 1.9067 trillion won for the AI sector, increasing the government’s original proposal by 61.8 billion won to strengthen the country’s global AI competitiveness. The Ministry of Science and ICT said the funding would be used to swiftly advance initiatives aimed at strengthening national AI competitiveness and positioning the country among the world’s top three AI leaders.
The funding is focused on three priorities: expanding AI computing infrastructure, advancing next-generation AI models and developing world-class talent. The largest allocation, 1.6341 trillion won, will be used to secure 10,000 advanced GPUs by the end of the year, alongside the leasing of a further 3,000 GPUs from the private sector to expand access.
A further 213.6 billion has been allocated to the proposed World Best LLM Project, under which five leading domestic AI teams will receive up to three years of support, including access to GPUs, high-quality datasets and specialist personnel. The Ministry will also launch the AI Pathfinder Project, offering grants of up to 2 billion won annually to attract leading international AI researchers.
Science and ICT Minister Yoo Sang-im said the funding comes at a pivotal moment as countries intensify competition for AI leadership. He said the government would pursue an all-out effort spanning advanced technology, talent development and AI adoption to establish South Korea among the world’s top three AI powers.
Why does it matter?
The supplementary budget demonstrates how governments are increasingly treating AI as strategic national infrastructure rather than simply an innovation policy issue. By investing simultaneously in computing capacity, foundation models and talent, South Korea is seeking to strengthen its long-term competitiveness in a global race increasingly defined by access to GPUs and skilled researchers.
The initiative also highlights that leadership in AI depends on more than financial investment alone. Competition for advanced chips and world-class talent has become increasingly intense, meaning the success of South Korea’s strategy will depend on how quickly it can translate funding into deployable infrastructure, cutting-edge research and commercial innovation.
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