Digital Public Services

What if you could build a world class AI. Without ever seeing real data? For years, this has been the catch 22 of public sector transformation. We want to use AI to solve huge challenges like fraud, but the risk of exposing real citizen data has been a hard stop. Progress has been trapped between the promise of innovation and the duty of privacy. A new white paper from HM Revenue & Customs, however, offers a brilliant solution They are training advanced fraud detection models without ever using real taxpayer information, all thanks to synthetic data. It's artificially generated information that mirrors the statistical patterns of a real dataset, a high fidelity, privacy safe replica that allows teams to build, test, and innovate with complete freedom. This is a true game changer for government delivery. → It unlocks innovation, allowing teams to build models without navigating months of complex data access approvals. → It guarantees citizen privacy by design, building the public trust needed for wider AI adoption. → It accelerates project timelines, moving from theory to a functioning model in a fraction of the time. This update from HMRC sets out a new blueprint for responsible innovation across the public sector. It proves we can be both data driven and privacy centric. #AI #DataPrivacy #HMRC

The G7 Toolkit for Artificial Intelligence in the Public Sector, prepared by the OECD.AI and UNESCO, provides a structured framework for guiding governments in the responsible use of AI and aims to balance the opportunities & risks of AI across public services. ✅ a resource for public officials seeking to leverage AI while balancing risks. It emphasizes ethical, human-centric development w/appropriate governance frameworks, transparency,& public trust. ✅ promotes collaborative/flexible strategies to ensure AI's positive societal impact. ✅will influence policy decisions as governments aim to make public sectors more efficient, responsive, & accountable through AI. Key Insights/Recommendations: 𝐆𝐨𝐯𝐞𝐫𝐧𝐚𝐧𝐜𝐞 & 𝐍𝐚𝐭𝐢𝐨𝐧𝐚𝐥 𝐒𝐭𝐫𝐚𝐭𝐞𝐠𝐢𝐞𝐬: ➡️importance of national AI strategies that integrate infrastructure, data governance, & ethical guidelines. ➡️ different G7 countries adopt diverse governance structures—some opt for decentralized governance; others have a single leading institution coordinating AI efforts. 𝐁𝐞𝐧𝐞𝐟𝐢𝐭𝐬 & 𝐂𝐡𝐚𝐥𝐥𝐞𝐧𝐠𝐞𝐬 ➡️ AI can enhance public services, policymaking efficiency, & transparency, but governments to address concerns around security, privacy, bias, & misuse. ➡️ AI usage in areas like healthcare, welfare, & administrative efficiency demonstrates its potential; ethical risks like discrimination or lack of transparency are a challenge. 𝐄𝐭𝐡𝐢𝐜𝐚𝐥 𝐆𝐮𝐢𝐝𝐞𝐥𝐢𝐧𝐞𝐬 & 𝐅𝐫𝐚𝐦𝐞𝐰𝐨𝐫𝐤𝐬 ➡️ focus on human-centric AI development while ensuring fairness, transparency, & privacy. ➡️Some members have adopted additional frameworks like algorithmic transparency standards & impact assessments to govern AI's role in decision-making. 𝐏𝐮𝐛𝐥𝐢𝐜 𝐒𝐞𝐜𝐭𝐨𝐫 𝐈𝐦𝐩𝐥𝐞𝐦𝐞𝐧𝐭𝐚𝐭𝐢𝐨𝐧 ➡️provides a phased roadmap for developing AI solutions—from framing the problem, prototyping, & piloting solutions to scaling up and monitoring their outcomes. ➡️ engagement + stakeholder input is critical throughout this journey to ensure user needs are met & trust is built. 𝐄𝐱𝐚𝐦𝐩𝐥𝐞𝐬 𝐨𝐟 𝐀𝐈 𝐢𝐧 𝐔𝐬𝐞 ➡️Use cases include AI tools in policy drafting, public service automation, & fraud prevention. The UK’s Algorithmic Transparency Recording Standard (ATRS) and Canada's AI impact assessments serve as examples of operational frameworks. 𝐃𝐚𝐭𝐚 & 𝐈𝐧𝐟𝐫𝐚𝐬𝐭𝐫𝐮𝐜𝐭𝐮𝐫𝐞: ➡️G7 members to open up government datasets & ensure interoperability. ➡️Countries are investing in technical infrastructure to support digital transformation, such as shared data centers and cloud platforms. 𝐅𝐮𝐭𝐮𝐫𝐞 𝐎𝐮𝐭𝐥𝐨𝐨𝐤 & 𝐈𝐧𝐭𝐞𝐫𝐧𝐚𝐭𝐢𝐨𝐧𝐚𝐥 𝐂𝐨𝐥𝐥𝐚𝐛𝐨𝐫𝐚𝐭𝐢𝐨𝐧: ➡️ importance of collaboration across G7 members & international bodies like the EU and Global Partnership on Artificial Intelligence (GPAI) to advance responsible AI. ➡️Governments are encouraged to adopt incremental approaches, using pilot projects & regulatory sandboxes to mitigate risks & scale successful initiatives gradually.

#AI in the public sector? And yet it moves! And it’s a prime example of how technological advancement requires the highest social and ethical standards. “Ethical Integration in Public Sector AI”: the new IAB X Center for Responsible AI Technologies study is out. It addresses the ethical design of AI in the public sector, with a focus on #PublicEmploymentServices (PES). While AI is increasingly employed to streamline administrative processes and improve service delivery, its application in employment mediation raises fundamental concerns regarding #fairness, accountability, and democratic legitimacy. The EU AI Act has further underscored the urgency of addressing these challenges by classifying employment-related AI systems as high-risk. We examine how ethical and social considerations can be systematically embedded in the development and implementation of public sector AI. Using the German PES as a case study, we introduce the “Embedded #Ethics and Social Sciences” approach, which integrates ethical reflection and practitioner involvement from the outset. Qualitative insights from interviews with caseworkers highlight the socio-technical challenges of implementation, particularly the need to reconcile efficiency with citizen trust. We propose concrete design elements emerging from the integration of ethical and social considerations into system development: data ethics, bias, fairness, explainable AI. The approach supports compliance with new regulatory requirements but also strengthens human oversight and shared decision-making.

Over the past decade, India has developed a distinct approach to digital and AI governance (different from the EU, US, and China) centered on population-scale public infrastructure. This model integrates digital identity, payments, authentication, and data-sharing into welfare and health systems. It now underpins insurance enrollment, provider payments, disease surveillance, telemedicine, and pharmaceutical supply chains for hundreds of millions of people. This approach shows how AI and digital systems can be aligned with universal health coverage while strengthening capacity for administration and innovation. This is how AI can work for #HealthForAll. The implications can go well beyond low- and middle-income countries (LMICs), with key lessons for health systems struggling with fragmentation and platform dependence. It has been a pleasure to work on this Think Global Health article with Ilona Kickbusch, Anurag Agrawal and Khartik Adapa . Do let us know what you think! https://lnkd.in/d4bTtZ3Q

Digital Systems and the SDGs 🌎 Digital infrastructure plays a growing role in advancing the Sustainable Development Goals. It enables new forms of data-driven decision-making, cross-sector efficiency, and real-time monitoring. Its integration into core systems—agriculture, health, energy, governance—is increasingly fundamental, but not without complexity. Precision agriculture uses drone imagery, AI forecasting, and sensor networks to optimize inputs and reduce losses. These systems improve productivity and resource efficiency but also introduce risks related to data ownership, scalability in low-connectivity zones, and long-term maintenance requirements. In public health, platform-based models accelerate vaccine development and distribution. Digital health records, logistics tools, and analytics platforms improve coordination. Still, challenges persist around data privacy, interoperability, and uneven infrastructure across regions. Education technology platforms expand access to content, skills, and certification. When designed for offline use and local relevance, they increase reach. Without these adaptations, they risk reinforcing disparities in digital access, language, and curriculum alignment. Smart grids, predictive maintenance systems, and IoT integration support low-carbon energy transitions. These solutions require high-quality connectivity and materials with environmental costs. Deployment should account for embodied emissions and responsible sourcing. Circular economy strategies rely on blockchain, traceability tools, and product passports to close material loops. While these systems improve transparency and compliance, they depend on energy-intensive infrastructure and require governance to ensure data integrity and accessibility. In urban planning and governance, real-time data platforms and digital services can improve mobility, public service delivery, and institutional performance. Implementation must address algorithmic bias, cybersecurity, and platform lock-in risks. This overview does not fully reflect the broader implications of artificial intelligence. As AI becomes more integrated across sectors, its impact on labor, decision autonomy, environmental footprint, and ethical governance will be critical areas to assess. The conversation must move beyond functionality to address long-term systems impact. #sustainability #sustainable #esg #climatechange #climateaction #sdgs

"Fathom’s latest report, AI at the Crossroads: Public Sentiment and Policy Solutions... draws on two national surveys, includes data from 30 focus groups and qualitative interviews, and features takeaways from 150 conversations with leaders across AI, the business community, and civic groups... Four Key Findings ­ 💡 The public is more aware of AI than other key federal issues, but is unsure about what it will mean for them. Over 77% of voters are aware of AI, but they are unsure about its societal impact. Voters are equally excited and concerned about AI’s potential. - Voters display confusion when deciding how much they would trust AI to perform daily tasks or take actions on their behalf. - Generally, voters are more comfortable with AI assisting with tasks than making decisions. - For example, 66% are comfortable with AI analyzing data for schoolwork, but only 26% are comfortable with AI making a purchase using their credit card. - Moreover, 81% are concerned about AI making decisions without human oversight, and they believe accountability and safety measures are essential. The biggest concerns arise when AI could make life-or-death decisions: - 82% of voters are concerned about AI making combat decisions. - 80% are concerned about AI performing surgeries or other medical procedures. 💡 The public wants to balance innovation with the creation of key guardrails, and their priorities do not fall along party lines. Misinformation, deepfakes, privacy, and AI decision-making without human oversight are top concerns for voters. Voters overwhelmingly support specific guardrails to address these issues, including: - Preventing AI interference in elections (84% support) - Ensuring human oversight (84% support) - Protecting data privacy (83% support) - Combating misinformation (82% support) Doomsday scenarios are viewed as alarmist and not compelling. The public breaks from typical partisan tendencies on AI, with: - Democrats doubting government efficacy. - Republicans acknowledging a role for regulation. 💡 The public is concerned about how the tech sector and government are advancing AI. ... - Public trust in the tech sector is waning. Voters are split in their confidence in the companies developing these technologies and worry they will prioritize profit and speed over safety - A strong majority (68%) believe the government has a role in regulating AI, but over half (56%) do not trust the government to regulate it properlyc 💡 The public is looking for a new model of leadership — we need to build a bigger table. ... - Academics and ethicists are favored for their commitment to safety over corporate interests, while elected officials with expertise in technology are seen as crucial for implementing effective regulations - Additionally, the general population should have input, along with professionals from various affected industries, to create a comprehensive and balanced approach to AI governance."

🔍 I've been thinking deeply about what makes data-powered governance truly effective. After some observation and some experience, I've identified three critical ingredients – what I humbly call the "Three D's". 📊 Data Exchange Platforms: The foundation that enables innovation through open data sharing and collaborative models. Estonia's X-Road has revolutionized public services by creating a secure data exchange layer connecting government databases. Citizens can access nearly all government services online, with 99% of public services available digitally. Singapore's Smart Nation Sensor Platform integrates data from sensors and IoT devices across the city to optimize everything from traffic flow to energy consumption. 📜 Data Policies: The essential guardrails that establish trust. The European Union's GDPR has set a global standard for data protection, enhancing citizen trust while creating a framework for responsible innovation. Closer home, the DPDP will start to set benchmarks for data-centric guardrails for a massive, diverse, and data-rich country like India. 🧩 Decision-Support Systems: The mechanisms that transform data into action. South Korea's COVID-19 response leveraged their Epidemic Investigation Support System to enable rapid contact tracing while maintaining transparency with citizens. Also, New Zealand's Integrated Data Infrastructure connects data across government agencies to inform policy decisions with robust economic analysis, resulting in more targeted and effective social programs. 💡 When these 3D's are combined deftly by the public-sector, citizen-centric governance becomes the cornerstone for any government. For the scale India operates at, it's a very good opportunity to show the way for the Global South. 🤔 I think we're at that inflection point with the recent announcement of AI Kosha and the DPDP, and they can help safely incubate innovative solutions that will optimize the delivery of government schemes, thereby ensuring timely, targeted assistance for citizens. Thoughts? #DigitalTransformation #PublicSector #Innovation #DataStrategy

When disaster strikes, digital public infrastructure (DPI) can rapidly transform public services. This is the foundational digital capabilities nations provide to facilitate efficient and secure interactions within society, such as proving identity, paying for goods and services, or sharing data. DPI is a country’s digital backbone, rapidly enabling modern and responsive government, increasing inclusion and economic participation, and stimulating innovation across all sectors. 🚨 But at a time of national emergency, DPI can deploy national-scale digital services that meet citizens’ needs, with the cost-effectiveness, scale, and flexibility needed to meet the unfolding situation. 🎥 This video and blog https://lnkd.in/gRKZ5PZM, written by Dasun Hegoda, share a great example: when fuel shortages paralyzed 🇱🇰 Sri Lanka 🇱🇰 in 2022, the national Information Communication Technology Agency of Sri Lanka leveraged Amazon Web Services (AWS) Cloud & used DPI to get the country moving. Developed in just three weeks, the innovative QR-code-based National Fuel Pass eliminated 5-day queues, processed 11 million transactions, and cut fuel import costs from $500 million to $240 million USD. Pete Herlihy Poppy H. Watch: https://lnkd.in/gtxPgG5H

In an era where data is the new oil, will governments settle for static dashboards — or build living, self-improving systems that shape the future? The UAE has answered this question boldly. Under the leadership of HH Sheikh Mohammed Bin Rashid Al Maktoum, the UAE has launched an AI-powered federal performance measurement system that moves beyond simply tracking progress it engineers national resilience and long-term competitive advantage. Why is this necessary? Global trends demand urgent government transformation: • By 2030, AI will add USD 15.7 trillion to the global economy through efficiency and innovation. • Up to 70% of public sector processes can be automated, cutting errors and manual work. • Inefficiencies cost governments USD 3 trillion annually, draining resources needed for strategic priorities • Crises now escalate five times faster than two decades ago, making static systems obsolete (OECD). What is the UAE doing differently? Unlike most governments that rely on periodic, historical data reviews, the UAE’s system focuses on real-time intelligence and predictive foresight. Key components include: • Advanced AI algorithms analyzing billions of data points across economic, health, environmental, and security indicators. • Predictive modeling to simulate policy impacts and stress-test national strategies. • Dynamic dashboards integrating data across ministries, providing leaders with a real-time, unified national operating picture. • Adaptive resource allocation, enabling budget and manpower shifts within days, not months. What are the concrete outcomes? • Economic resilience: AI to add AED 335 billion (USD 91 billion) to GDP by 2030 (13.6% of total output). • Service efficiency: Up to 30% cost savings, 40–60% faster services, and 90% fewer errors. • Resource optimization: 25–35% more efficient cross-ministry budgets, freeing billions. • Policy agility: Policy cycle times cut by up to 50% for rapid response. • Citizen satisfaction: Up to 35% higher satisfaction and stronger public trust. • Global competitiveness: UAE ranks top 10 worldwide in AI readiness, reinforcing digital leadership. How does this shape the future? The UAE is not just measuring performance: it is building a living, adaptive system of governance that: • Anticipates and mitigates disruptions before they escalate. • Dynamically reallocates resources for maximum national impact. • Designs policies with deep societal and economic insight. • Strengthens trust through transparent, citizen-focused services. “Continuous improvement is a core habit of government work, because stopping the development of our tools means falling behind. Our motto: ‘There is no perfect system, but everything can be developed and improved.’” — HH Sheikh Mohammed Bin Rashid Al Maktoum The UAE is not just building a nation it is setting a global standard for excellence, resilience, and future-ready leadership. The UAE has made its choice. The world is watching.

Estonia is laying the legal foundation for the AI- and agentic-state era Digital government does not end at e-services. As the state becomes proactive, data-driven, and increasingly agent-based, we need clear rules for how automated and AI-supported decisions are made — and how people’s rights are protected. That’s why we are updating the Administrative Procedure Act to create a technology-neutral legal framework for automated administrative procedures. This matters far beyond automation. It is a prerequisite for the agentic state — where AI agents can: • proactively trigger services • execute well-defined administrative steps • coordinate across institutions • act on behalf of the state within clearly bounded mandates What does the reform enable in practice? • Authorities may use automated and AI-based systems for administrative decisions • Automated decisions can support even rule-based discretion, without removing human accountability • People always retain the right to human contact, explanations, and to challenge outcomes • Decisions must be transparent by design. Citizens are informed when automation or AI is used, which data sources were involved, and what decision logic applied • Sensitive data is protected through explicit legal safeguards • Appeals and reviews always involve a human decision-maker This is not about replacing officials with machines. It is about enabling trusted AI agents to operate inside the rule of law, with clear boundaries, auditability, and legal responsibility. Without this legal clarity, an agentic state cannot scale safely. With it, Estonia can truly move from reactive services to event-based, anticipatory, and citizen-centric governance — while strengthening trust, legal certainty, and democratic oversight. Read more about the planned legal changes here, all feedback is welcome: https://lnkd.in/g93Brfjx