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Australian Finance Department releases internal AI guidance

Australia’s Department of Finance has publicly released internal guidance on generative AI under the Freedom of Information Act 1982, outlining how staff and contractors should use AI tools in their work.

The guidance, dated March and April 2026, applies to tools including Microsoft 365 Copilot Chat, Microsoft 365 Copilot full licences, and public generative AI services such as ChatGPT, Claude, and Gemini. It says AI tools can improve productivity and service delivery, but also carry risks that must be understood and managed.

Staff intending to use AI tools must complete the APS Academy’s AI in Government Fundamentals course. Staff are also encouraged to build prompting skills in a secure environment through GovAI’s Interactive Learning Environment and discuss approved AI use cases with managers.

The guidance says staff must use generative AI safely, responsibly, and ethically, in line with departmental policies and APS values. The guidance states that AI should support, rather than replace, human judgement and that final decisions must always be made by people, not AI systems.

The document also sets limits on the information that can be entered into AI tools. Public generative AI tools may be used with non-sensitive official and unofficial information, but staff must not enter personal, sensitive, classified or protected information. Copilot Chat and Copilot full licence in web mode are also restricted from use with personal, sensitive, classified, or protected information.

Finance’s enterprise-grade Copilot full licence operating in work mode permits broader use within the department’s ICT environment, including sensitive and protected information where an AI use case has been formally registered. Staff seeking to use personal information with Copilot full licence must complete a Privacy Impact Threshold Assessment and consult the Privacy Team.

The guidance also requires staff to register some AI use cases through Finance’s AI Use Case Register. Registration is required for certain uses involving personal, sensitive, classified, or protected information, while paid AI tools or systems require consultation or approval before procurement.

Staff are required to disclose AI use when AI-generated content significantly influences decision-making, could reasonably be mistaken for human-generated content, has not been reviewed by a subject-matter expert, or where legal or ethical obligations require disclosure.

The department says AI use is overseen by an AI Governance Committee responsible for promoting AI strategy, supporting safe implementation, advising on ethical, legal and social responsibilities, and ensuring compliance with government legislation, regulations and standards.

The guidance says Finance governs AI in line with the Digital Transformation Agency’s Policy for the responsible use of AI in government, Australia’s AI Ethics Principles, the Pilot Australian Government AI assurance framework, and the Protective Security Policy Framework. It says the department has limited its use of AI to low-risk use cases.

Why does it matter?

The guidance provides a practical example of how governments are translating high-level AI principles into operational rules for everyday use. Rather than focusing solely on ethics frameworks, it addresses concrete issues such as training requirements, approved tools, data handling, disclosure obligations and governance processes.

The document also highlights a broader challenge facing public administrations worldwide. As generative AI becomes part of routine government work, agencies must balance productivity gains with privacy, security, transparency and accountability requirements. Australia’s approach illustrates how governments are seeking to enable AI adoption while maintaining human oversight and limiting risks associated with sensitive information.

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Armenian finance minister highlights AI’s economic potential and risks

Armenia’s Finance Minister Vahe Hovhannisyan said AI could support economic growth while also creating new economic and labour-market challenges. He made the comments during a parliamentary discussion on the performance of the 2025 state budget.

Hovhannisyan said the impact of AI is being widely debated internationally and that governments around the world are actively exploring its economic implications. He was responding to questions about AI’s potential effect on GDP growth and the expansion of the tax base.

The minister cited international estimates suggesting that AI adoption could add approximately 0.8 to 1 percentage point to economic growth. He said AI has the potential to generate new forms of employment while supporting productivity and economic growth.

At the same time, Hovhannisyan warned that AI could disrupt existing jobs and create adjustment challenges for labour markets. The remarks were made during discussions on Armenia‘s 2025 budget performance, as the government’s 2026 budget projects economic growth of 5,4%.

Why does it matter?

The comments reflect a broader global debate about AI’s economic impact. Policymakers increasingly view AI as a potential driver of productivity, innovation and economic growth, while also recognising the possibility of labour-market disruption and changing workforce demands.

For emerging economies such as Armenia, the challenge is not only adopting AI technologies but also ensuring that workers and businesses can benefit from them. The long-term impact of AI on growth, employment and public finances will depend on investment, skills development and the ability to adapt to technological change.

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European Commission opens applications for RAISE AI research advisory board

The European Commission has opened applications for the RAISE High-Level Academic Advisory Board, inviting leading researchers in AI and AI-enabled science to help shape Europe’s future AI research agenda.

The advisory board will support the implementation of the EU’s AI in Science Strategy and provide independent scientific guidance on the development of RAISE (Resource for AI Science in Europe).

RAISE was launched in 2025 under Horizon Europe to strengthen European leadership in both fundamental AI research and the application of AI across scientific disciplines.

The Commission is seeking academics with expertise in AI research or experience applying AI in fields such as medicine, climate science and advanced materials. Board members will provide strategic recommendations on research priorities, governance structures, benchmarks and framework conditions needed to accelerate AI-enabled scientific discovery.

Through RAISE, the EU aims to bring together leading researchers, computational resources, data and funding within a coordinated ecosystem that supports scientific excellence and strengthens Europe’s position in global AI research and innovation.

Why does it matter?

The initiative reflects growing recognition that AI is becoming a foundational tool for scientific discovery across disciplines ranging from healthcare and climate research to materials science and physics. Governments are increasingly investing in AI research infrastructure to ensure that researchers have access to the computing power, data and expertise needed to remain globally competitive.

The advisory board also highlights Europe’s ambition to play a larger role in shaping the future of AI-enabled science. By coordinating talent, infrastructure and funding through initiatives such as RAISE, the EU aims to strengthen both its scientific capacity and its position in the global race for AI innovation.

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Japanese researchers develop interpretable AI for materials discovery

Researchers in Japan have developed an interpretable AI method to explain how AI models make predictions in materials discovery. The method analyses features learned by a trained AI model and uses them to identify relationships between atomic structure and optical spectra.

The study was led by researchers from the Institute of Science Tokyo, in collaboration with Tohoku University. The work is expected to be published in the journal Advanced Intelligent Discovery.

AI is increasingly used in materials research to predict how materials behave based on atomic structure. Such models can accelerate materials discovery and reduce reliance on trial-and-error experimentation, but many operate as black boxes, making it difficult to understand how they arrive at specific predictions.

The researchers addressed this problem by analysing a trained AI model that predicts optical absorption spectra from atomic structural data. They extracted features from the model’s internal layers and clustered materials according to shared structural and spectral characteristics.

The team used an atomistic line graph neural network trained on data from 2,681 metal oxides, chalcogenides, and related compounds. The clustering process classified materials into groups sharing structural characteristics such as elemental composition, atomic coordination, bond lengths, bond angles and similar spectral signatures.

According to the researchers, the model learned meaningful relationships between atomic structure and material properties without being explicitly provided oxidation states or electronic configurations as input. The interpretable AI method could therefore help researchers identify the factors behind desired spectral shapes and support more rational materials design.

The approach could also be applied beyond optical absorption spectra. Researchers said the approach could also help explain how atomic arrangements influence other material properties under varying conditions, such as temperature and pressure, opening new possibilities for designing materials with targeted characteristics.

Why does it matter?

One of the main challenges facing the use of AI in scientific research is explainability. While AI systems can identify patterns and generate accurate predictions, researchers often need to understand the reasoning behind those predictions before they can confidently apply them in experimental settings.

By revealing how AI models connect atomic structures with material properties, interpretable AI could make machine learning a more effective tool for scientific discovery. The approach may help accelerate the development of advanced materials for applications ranging from renewable energy and electronics to sensors and next-generation manufacturing, while improving trust in AI-assisted research.

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EU AI Board reviews AI Act implementation and tech sovereignty agenda

The EU AI Board held its eighth meeting to review progress on AI Act implementation and discuss wider priorities in the EU’s AI strategy.

The meeting took place under the chairmanship of the Cypriot Presidency of the EU Council. The presidency also announced that Moldova had been granted observer status on the AI Board.

The European Commission presented its Tech Sovereignty Package, with a focus on the proposed Cloud and AI Development Act and its role in strengthening AI innovation, competitiveness and technological sovereignty in Europe.

The Board also reviewed the final version of the voluntary Code of Practice on labelling and marking AI-generated content. The code sets out practical steps to help providers and deployers of generative AI systems meet transparency obligations under the AI Act, which will apply from 2 August 2026.

Further discussions focused on the AI Act’s implementation architecture. The Commission presented the recently appointed Scientific Panel and AI Act Advisory Forum, which will support the Commission and the AI Board. Members also discussed progress in establishing national market surveillance authorities and endorsed additional documents prepared by an AI Board subgroup, which are expected to be published shortly.

Why does it matter?

The meeting shows the EU moving from AI Act adoption towards practical implementation. The discussion links several important pieces of the EU AI governance architecture: voluntary transparency tools, expert advisory bodies, national market surveillance authorities and broader industrial policy through the Tech Sovereignty Package. Together, these elements will shape how AI rules are coordinated, interpreted and enforced across the EU.

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EU and Brazil strengthen cooperation through new Digital Partnership

The European Union and Brazil have signed a new Digital Partnership to strengthen cooperation on shared digital policy priorities, including AI, data governance, digital infrastructure, connectivity, online platforms and digital public goods and services.

The partnership was signed in Brasília and is intended to raise EU-Brazil digital cooperation to a more strategic level. According to the European Commission, Digital Partnerships are a core instrument of the EU’s external digital policy and are used to structure cooperation with like-minded partners.

The agreement builds on more than two decades of EU-Brazil cooperation, including the EU-Brazil Strategic Partnership and the existing EU-Brazil Digital Dialogue. The two sides said the partnership will support joint work on resilient global supply chains, rules-based digital governance and wider sharing of the benefits of technological progress.

The signing follows the adoption of mutual EU-Brazil data adequacy decisions in January 2026, which allow personal data to flow freely and securely between the two jurisdictions without additional requirements. The Commission described those decisions as creating the world’s largest area of free and safe data flows, covering around 670 million consumers.

Future cooperation under the Digital Partnership will be developed through technical workstreams and high-level exchanges. The first Digital Partnership Council is expected to meet within the next year to set out a joint roadmap for cooperation.

Why does it matter?

The partnership strengthens digital cooperation between the EU and one of Latin America’s largest economies at a time when AI governance, data protection, online platforms and digital public infrastructure are becoming central to international relations. It also shows how the EU is using digital partnerships and data adequacy decisions to expand trusted digital cooperation beyond Europe, while promoting regulatory alignment, secure data flows and shared approaches to global digital governance.

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MIT researchers develop cooling system to cut data centre energy and water use

A startup founded by researchers from MIT has developed a nuclear-inspired cooling system designed to improve data centre energy efficiency while reducing water consumption. The technology targets one of the fastest-growing sources of electricity demand, as the rapid expansion of AI infrastructure drives increased computing requirements.

Ferveret’s system uses a specialised liquid to immerse servers, replacing traditional air-based cooling methods that account for a significant share of data centre energy consumption. Its Adaptive Phase Cooling approach improves heat transfer through controlled bubble formation, increasing efficiency while reducing reliance on water-intensive cooling systems.

The company reports computational efficiency gains of up to 15% compared with existing liquid cooling technologies, alongside improved overall performance when combined with power optimisation software. Ferveret is already testing its system with several data centre operators and AI hardware companies as it moves towards wider commercial deployment.

The startup says its modular design enables easier integration into existing facilities while allowing data centres to operate more effectively in regions with limited water resources. By reducing energy waste and improving heat management, the technology aims to support the growing demand for AI computing without further increasing environmental strain.

Why does it matter?

The rapid growth of AI is driving unprecedented demand for computing power, placing increasing pressure on electricity grids, water supplies and data centre infrastructure. Cooling systems are a major contributor to both energy consumption and operating costs, making efficiency improvements a growing priority for the technology sector.

Innovations such as liquid immersion cooling could help reduce the environmental footprint of AI infrastructure while supporting continued growth in computing capacity. As governments and companies seek to balance AI expansion with sustainability goals, advances in cooling, power management and resource efficiency are becoming an increasingly important part of the broader AI ecosystem.

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Singapore warns of Microsoft impersonation scams causing major losses

The Singapore Police Force (SPF) and the Cyber Security Agency of Singapore (CSA) have warned the public about technical support scams that impersonate Microsoft. Authorities said at least 10 cases had been reported since February 2026, with total losses exceeding S$1.7 million.

In this scam variant, victims typically encounter a pop-up alert in their web browser. The alert falsely appears to originate from Microsoft and claims that the user’s device has been hacked or compromised.

Victims are then instructed to contact a so-called technical support officer through an internet-based phone number. After making contact, victims may be transferred to another scammer posing as a police officer, who claims that their device has been used for criminal activities such as money laundering.

Authorities in Singapore said victims may be instructed to make bank transfers, provide banking credentials, or grant remote access to their devices. In some cases, scammers asked victims to download remote access applications or click links that allowed them to take control of bank accounts.

SPF and CSA advised members of the public to verify alerts through official software provider channels. They noted that Microsoft does not include phone numbers in error or warning messages, and that users should not call numbers displayed in suspicious pop-ups or click links or buttons within such alerts.

People who believe they have fallen victim to the scam are advised to disconnect their computer from the internet, contact their bank, remove applications installed under the scammer’s instructions, and run an anti-virus scan. They should also change passwords and banking credentials using a trusted device, remove unauthorised payees, and report the incident to the police and CSA’s SingCERT.

Why does it matter?

Technical support scams remain one of the most effective forms of cyber-enabled fraud because they combine social engineering, impersonation and remote access techniques. By exploiting trust in well-known brands such as Microsoft and creating a sense of urgency, scammers can persuade victims to hand over sensitive information or direct access to their devices.

The cases also highlight how cybersecurity and financial security are increasingly interconnected. Basic cyber hygiene practices, such as verifying security alerts through official channels, avoiding unsolicited remote access requests and reporting incidents quickly, can help prevent account compromise and reduce financial losses.

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Spanish minister says AI regulation boosts competitiveness and trust

Spain’s Minister for Digital Transformation and Public Function, Óscar López, said that AI regulation strengthens competitiveness rather than discouraging investment. Speaking at the Foro Talento España event organised by TRIVU, he argued that trust is becoming a key factor in the development and adoption of AI.

López pointed to OpenAI’s decision to open its first office in Spain as evidence that AI regulation can coexist with innovation and investment. He said Spain’s approach helps create a more predictable and trustworthy environment for businesses and technology development.

The minister also highlighted government investments in digital skills and talent development. He cited initiatives including the National Digital Skills Plan, university programmes focused on AI and cybersecurity, and plans to recruit 1,600 ICT specialists for the public sector.

These efforts have contributed to growth in higher education, technology training and STEM employment. Speaking in Madrid, López said continued investment in talent, digital skills and emerging technologies will be essential as AI and other advanced digital sectors continue to evolve in Spain.

Why does it matter?

The relationship between AI regulation and innovation remains a central policy debate worldwide. While some argue that regulation could slow investment and technological development, others contend that clear rules can increase trust, reduce uncertainty and encourage long-term adoption.

Spain’s position reflects a growing European approach that views regulation and innovation as complementary rather than competing objectives. By combining AI governance measures with investments in skills, education and digital talent, policymakers are seeking to build an environment that supports both technological development and public trust.

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Microsoft president says AI’s future should be shaped by people, not technology alone

Microsoft Vice Chair and President Brad Smith has argued that the future impact of AI should be shaped by people rather than technology alone, emphasising the importance of human agency, creativity and the dignity of work.

In a recent blog post, Smith said concerns expressed by university graduates about AI’s impact on employment should be taken seriously by the technology sector.

Smith also noted that younger generations remain among the most active users of AI technologies but are increasingly questioning how AI will affect jobs, careers and society. He argued that graduates are sending a clear message that AI should support human capabilities instead of determining the role of people in the workforce.

The article draws on historical examples of technological disruption, including photography, computing and automation, arguing that new technologies have often transformed work rather than eliminated human creativity and ambition.

Smith acknowledged concerns about entry-level employment, workforce restructuring and economic uncertainty, while suggesting that AI adoption is likely to unfold over decades rather than over a short period.

Microsoft argues that individuals should focus on combining expertise in their chosen fields with AI literacy. The company also emphasises the importance of uniquely human skills such as creativity, curiosity, communication, compassion and judgement.

For organisations, Smith recommends using AI to strengthen institutional knowledge and productivity while retaining control over proprietary data, intellectual property and strategic decision-making.

Why does it matter?

The debate over AI’s impact on employment has become one of the central questions in technology policy and economic planning. While some forecasts focus on job displacement, others argue that AI will primarily transform how work is performed, creating demand for new skills and roles while reshaping existing occupations.

Smith’s comments offer insight into how a leading AI developer views the long-term transition. His emphasis on augmentation, workforce adaptation and human agency reflects a broader industry narrative that AI should enhance rather than replace human capabilities, while highlighting the growing importance of digital skills, lifelong learning and public participation in decisions about AI deployment.

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