Technological Impact on Policy Creation

To create good policy you need responsible foresight, enabling ethical, sustainble, accountable future design. AI now can massively enable human-centered responsible foresight, in helping address uncertainty, assess risks, and set policies for creating better futures. María Pérez Ortiz's new paper "From Prediction to Foresight: The Role of AI in Designing Responsible Futures" describes responsible foresight in policy and the role of computational foresight tools. Notable approaches to using AI in responsible foresight include: 🤝 Participatory Futures for Inclusive Planning. Engaging diverse stakeholders in foresight practices democratizes the future-planning process. AI tools streamline public participation by analyzing preferences, simulating collective decisions, and creating urban plans that reflect community values, fostering equity and resilience. 🧠 Superforecasting for Precision and Insight. Superforecasting uses disciplined reasoning and probabilistic thinking to predict uncertain events. AI-powered assistants improve human forecasting accuracy by 23%, aggregating data and refining predictions through collective intelligence and advanced analytical models. 🌐 World Simulation for Systemic Insights. Advanced modeling frameworks simulate interconnected global systems, enabling policymakers to test "what-if" scenarios. AI accelerates these simulations, providing precise forecasts and dynamic platforms to visualize the long-term consequences of policy decisions across economic, social, and environmental domains. ⚙️ Simulation Intelligence for Decision Optimization. By integrating AI with high-fidelity simulations, simulation intelligence explores complex systems to uncover optimal strategies. This tool assists in crafting effective policies for urban planning, sustainable agriculture, and climate resilience, offering actionable pathways for addressing systemic challenges. 📜 AI-Assisted Narrative Techniques. Large language models contribute to speculative futures by generating detailed "value scenarios" that integrate ethical, technological, and societal considerations. These AI-driven narratives enable policymakers to visualize desirable outcomes and evaluate potential trade-offs. 🔗 Hybrid Intelligence for Enhanced Foresight. Combining human creativity with AI’s computational strengths creates a robust foresight framework. Intuitive interfaces, explainable AI, and participatory design ensure that tools remain transparent and aligned with ethical considerations, empowering policymakers to navigate complex challenges collaboratively. ♻️ Iterative Foresight with Feedback Loops. Continuous monitoring and real-time adaptation enhance foresight processes. AI’s ability to process evolving data and generate actionable insights ensures policies remain responsive, flexible, and aligned with long-term objectives. The power of AI in assisting foresight is just beginning to come to fruition.

#cbdc | #digitaltechnologies | #RBI : Reserve Bank of India (RBI) Deputy Governor Michael Patra on "Harnessing Digital Technologies in Central Banks: Opportunities and Challenges" Opportunities : (1) First, we live in an age of data–driven policy making. #centralbanks are repositories of enormous volumes of data. Therefore, data quality and data governance are of utmost importance to ensure that policy measures are apposite and effective. In this context, digital technologies, especially the newer ones including AI and #machinelearning , help to dive deep into existing data as well as unstructured and high-frequency information to carry out meaningful analyses. They help to detect trends and anomalies better and thereby provide useful insights on specific economic and financial situations as inputs for policy formulation. In essence, the synergy between structured data, rigorous reporting and AI amplifies the productivity of data-driven processes, reinforcing their importance in modern central banking. (2) central banks can use digital technologies, especially newly developed tools of big data analytics, for economic forecasting that is vital for forward-looking monetary policy assessments. (3) In the oversight of #financialmarkets , technological innovations can help trade repositories (TRs) to tackle data quality issues and increase the value of TR data to authorities and the public. Regulators need to be vigilant, however, about unexpected forms of interconnectedness between financial markets and institutions on account of applications of AI and ML. (4) regulatory compliance is another area which can significantly benefit central banks through RegTech and SupTech tools. With the increasing complexity of financial regulations, automating compliance processes through such tools, conducting risk assessments and monitoring transactions for potential violations could help sharpen compliance and ensure that financial institutions adhere to regulatory frameworks. This helps in reducing compliance costs for regulated entities, while improving the financial ecosystem as a whole. Machine readable regulations could be an additional synthesis in the usage of AI. Effective use of new technologies is expected to help to detect fraudulent actives in the system in a complex and interconnected environment. (5) Emerging technologies help central banks to design new products and services to cater to specific requirements. For instance, CBDC as a digital form of sovereign currency offers a secure and reliable medium of exchange. Challenges : (1) With the increasing use of AI, concerns arise about transparency, data biases, governance, privacy and the robustness of algorithms. The RBI has emphasised that data used for training of models should be extensive, accurate and diverse to rule out any prejudices and that #algorithms should be auditable.

A Canadian government department wanted to use AI to process visa applications faster. Before they could deploy, they had to complete an Algorithmic Impact Assessment. Question 15: "Could this system's decisions affect someone's legal rights?" Yes. Question 23: "Will decisions be automatically made without human review?" Partially. Question 31: "Does the system use machine learning trained on historical data?" Yes. Final score: Level 3 (High Impact) Requirements triggered: → Explainability for every decision → Human review for all rejections → Quarterly bias testing → Public audit trail The department couldn't deploy until these were in place. Six months later: The system processed applications 40% faster. But monitoring revealed something interesting: Applications from certain countries were flagged for review at 3x the rate predicted. Because the assessment was public, a researcher noticed this gap. Investigation revealed the AI learned patterns from old data when those countries had different visa requirements. System was retrained. Assessment was updated. Public report explained what was learned. This is what good governance looks like: Not rules preventing deployment. Not audits finding problems later. But transparency creating continuous learning. The Canadian approach proves something crucial: You don't need complex regulations. You need organizations to commit publicly to their AI's impact, then govern the gap between promise and reality. Simple. Transparent. Effective. Why isn't everyone doing this? #AIRegulation #AIPolicy #DigitalGovernance #TechPolicy #RegulatoryCompliance

Emerging applications of neurotechnology and their implications for EU governance - A technology foresight study This new report European Commission provides an overview of recent developments in neurotechnology — technologies capable of reading from or modifying activity in the central nervous system. Some devices record information from the brain, others deliver stimulation to it — and increasingly, some do both. These technologies are advancing rapidly and are expected to have a profound impact on society. In the near future, neurotechnology may revolutionise the way we approach a wide range of policy areas: not only health and research, but also education, employment, security, law enforcement, digital governance and fundamental rights. The report explores recent advances in both brain-monitoring and brain-stimulation technologies, many of which are already being integrated into consumer and clinical devices. It offers a horizon scan of emerging applications and uses this landscape as a basis for posing critical governance questions to EU policymakers. Kudos to the authors: Antonia Mochan, Beth Parkin, Ph.D, João Farinha, and Gwendolyn Bailey, Ph.D. #Neurotechnology #Foresight #StrategicForesight #EU #Governance #EmergingTech #HorizonScanning #TechnologyPolicy

I've debated with myself if I should write this post. Last week's government's drought advice included a recommendation to "delete old emails and pictures to save water at data centres." This kind of guidance shows why we urgently need more technologists across the public sector, and especially in the policy teams. It's not that this advice will not help, it will actively worsen the situation. Here's the thing: deleting old emails doesn't save water. Those emails sit in "cold storage" using virtually no energy. Worse, logging in to delete them actually creates more server activity and heat than leaving them alone. It's a bit like suggesting we burn books to reduce library heating costs. But this isn't really about drought policy - it's about something much bigger. We're living through the fastest technological transformation in human history. AI, quantum computing, biotech - these aren't distant future concepts anymore. They're reshaping our economy, our infrastructure, and our society right now. Yet many of our policy frameworks were designed for a different era. Clearly, even new policies are also sometimes designed for a different era! Government departments are full of incredibly smart people with deep expertise in economics, law, and public administration. What we're missing are enough people who can translate between the technical realities of our digital world and the policy decisions that shape it. We need people who understand both the technology and the Cabinet Office. People who can spot when well-intentioned advice might backfire. Who can help design regulations that actually achieve their intended outcomes. Who can anticipate the second and third-order effects of tech policy decisions. This isn't about criticizing anyone - it's about building stronger teams. The challenges we're facing as a country require all types of expertise working together. How do we attract more tech professionals into public service? And how do we help existing government teams access technical expertise when they need it? #GovTech #PublicService #TechPolicy #Innovation #Leadership

🔍 Spring Statement 2025: What It Means for the UK Tech Sector 🚀 The Spring Statement 2025 brings significant policy shifts and funding opportunities for tech businesses, startups, and investors. Here are the key highlights shaping the future of UK tech: 🔹 Defence Innovation 🛡️ 💰 £2.2B increase in defence spending—driving investment in emerging tech solutions 🚀 Launch of UK Defence Innovation (UKDI)—a new initiative to support high-tech defence startups 🤖 10% of the MoD’s equipment budget is now earmarked for cutting-edge technologies (AI, cybersecurity, autonomous systems) 💡 This signals a major opportunity for AI, robotics, and deep tech firms to secure government contracts and R&D partnerships 🔹 Digital Transformation & AI Growth 💻 🏛️ £3.25B Transformation Fund—aimed at AI-driven digital upgrades in public services 🚀 £42M for Frontier AI projects—focused on pioneering AI innovations in healthcare, finance, and infrastructure 📈 Increased government demand for GovTech, AI-driven automation, and cybersecurity solutions 🔹 R&D Tax Credits: Key Developments 💰 🧪 Ongoing consultations on R&D tax credits—ensuring they better support UK tech innovators ⚖️ Government committed to reforming & optimizing the tax credit system to drive business growth 💡 Tech firms should stay alert for potential updates impacting tax relief on software, AI, and deep tech R&D 🔹 Regulatory Environment: Less Red Tape 📜 ✅ The Regulation Action Plan is designed to simplify compliance and reduce administrative costs 🚀 Pro-business regulatory changes could create a more agile environment for startups & scaleups 📊 Digital regulatory frameworks may evolve—helping innovative companies navigate AI, data privacy, and fintech regulations 🔹 Taxation & Fiscal Updates 💡 📢 Changes to R&D tax credits & non-dom tax rules—tech founders & investors should review potential impacts 📊 The government is assessing corporate tax structures to enhance the UK's appeal for high-growth startups & venture capital 🔥 What This Means for UK Tech Businesses: ✅ Expansion & Growth: Defence, AI, and GovTech offer new revenue streams for innovative firms ✅ Investment Opportunities: Stronger R&D incentives & AI investments may attract VC funding & foreign capital ✅ Operational Efficiency: Simplified regulations & digitization could create a faster, more scalable business environment 🚀 The 2025 Spring Statement underscores the UK government’s focus on tech-led innovation—opening up exciting new opportunities for founders, investors, and scaleups. 👉 What are your thoughts? How will these updates impact your business? #SpringStatement #UKTech #Innovation #AI #GovTech #TechPolicy #Startups #VentureCapital #DigitalTransformation #RDtaxcredits #RegTech #FutureOfTech #InnovateUK #HMGovernment #UKStartups #TechFunding #LinkedinNews #Newable #InnovateUK #UKRI

A new president and Congress give us a chance to rethink our policies toward new technologies. When transformative technologies like crypto and AI come along, we should first try to understand their essence — examining the full range of possibilities they unlock, both good and bad — and then design policies that promote positive outcomes while minimizing risks. As I wrote in Read Write Own: The way to see the truth is to separate the essence of a technology from specific uses and misuses of it. A hammer can build a home, or it can demolish one. Nitrogen-based fertilizers help grow crops that feed billions of people, but they can also be used in explosives. Stock markets help societies allocate capital and resources where they can be most productive, but they also enable destructive speculative bubbles. All technologies have the capacity to help or harm... the question is, how can we maximize the good while minimizing the bad? The wrong way to approach new technologies is to shoehorn old policies into the new era using lawsuits and piecemeal rule making. These reactive approaches create uncertainty, fragment rules across jurisdictions, and take years or even decades to resolve. The previous administration's legal campaign against crypto demonstrated the flaws of this approach, failing both policy-wise and politically. Emerging industries can only be built on stable foundations. The internet was built on forward-thinking congressional legislation, including most prominently the 1996 Telecommunications Act. This created a cohesive national framework that persisted across multiple administrations and set a model for other countries to follow. As a result, the internet flourished, and the US led in its development. Today we are at a similar crossroads. We are now, finally, experiencing the big payoff of roughly 80 years of computing: accelerating progress in crypto, AI, biotech, robotics, and more. This is the time to craft thoughtful, comprehensive, modern frameworks that both acknowledge the promise and risks of these technologies and ensure the US leads the way.

I was considering what the key "game changer" aspects of AI might be for the public service, and I realised a few key things... Firstly, that AI has been, for many, a bit of a tipping point in realising they need to change - not because of the technologies themselves, but because it has been so disruptive it has forced folk to think, plan and start to respond. This isn't a bad thing :) Secondly, that this tipping point has also encouraged people to reconsider their operating models and tech/digital/data/AI more broadly, and to start to realise that efficiency agendas (efficiency for the sake of efficiency) and deficit planning (how do I use limited resources to address a diminishing proportion of the problem space) have both got in the way of actually maximising policy impact and mission outcomes. Real disruption can help people to rethink what "good" could be, and start to shift from deficit planning towards "how might we maximise our impactfulness" :) So, in short, the three game changer aspects I came up with were, in order of increasing value: 1) Support for people - some AI tools (inc Gen AI) are like a next generation spell check. They can be a helpful aid, but you wouldn't ask spellcheck to do your job (or create new policy). Some AI can help staff to engage broader, synthesise information a little easier, identify patterns, but it doesn't replace deep expertise or experience of staff. 2) To create sensing institutions - machine learning is great at identifying patterns which can be escalated for action in closer to real time. Rather than using data (and AI) to simply analyse the world to put into reports or dashboards, we can start to "sense" change (intended or unintended impacts, threats, etc) as it happens, to feed into more adaptive operating models with multi-disciplinary teams, enabling greater responsiveness to the world around us. 3) To scale our impact - rather than only assuring a diminishing percentage of our domains, we could use new tech and methods to engage with a dramatically increased proportion of our domains, which would also reduce the playground for things to go wrong. Consider the regulator who used to assure 100% of their regulated entities but now can only afford to assure the top 5%. Or the services delivered that measure their value only in customer experience, rather than how well the policies are working or community quality of life is improved. We could use AI to amplify and augment insights from staff, from data, from feedback loops with the public, but we also could use this moment in time to genuinely become mission oriented and outcomes obsessed, in how we plan programs, resourcing, investment prioritisation and structures. We need to measure "success" by the intended impact, outcomes and value delivered to the community, with efficiency as a guiding principle rather than a blunt instrument that values itself over the actual point or purpose of a public institution. Thoughts welcome :)

New Paper Alert! How can AI innovation improve social outcomes? In our article, David Watson, Luciano Floridi and I introduce “Sociotechnical Pragmatism” as a robust stance for policymakers and researchers who seek to reap the benefits of AI while managing the risks. https://rdcu.be/d4Val Realizing AI's potential to improve social and environmental conditions will require technological innovation, political leadership and good governance. Hence, sociotechnical pragmatism does more to promote social progress than either naive dogmatism or radical skepticism. Several key assumptions underpin sociotechnical pragmatism: 🌏 The pragmatic maxim. Technologies and policies should be judged by their success when applied in real-world contexts. Sociotechnical pragmatism privileges empirical technology assessments and evidence-based policymaking over abstract debates 🙋 Human agency. The future depends on our actions, the choices we make as individuals and the norms we enact as collectives. Sociotechnical pragmatism rejects any form of technological determinism, whether utopian or dystopian ⚖️ The need for trade-offs. Different values conflict and require trade-offs. The purpose of AI evaluations is not to guarantee ethical outcomes but to make visible tensions, give voice to stakeholders, and arrive at resolutions that (even when imperfect) are publicly defensible 🕸️ Sociotechnical approach. AI forms part of sociotechnical systems that involve other artifacts, people and organizations operating in dynamic environments. To ensure good governance, technical fixes must be complemented by legal and social interventions 🎯 Conceptual precision. “AI” is not a specific technology. It’s an umbrella term covering a diverse set of techniques to represent and solve problems computationally. For practical purposes, it is less important to define AI and more important to classify different systems 📊 The comparative baseline. AI systems, human decision-makers and bureaucracies have different strengths and weaknesses. A normative evaluation of AI’s merits and limitations must not be conducted in isolation but in relation to the available alternatives 🛠️ The logic of design. The impact of both technologies and policies is a matter of design. The question policymakers face is not whether to regulate AI but how to do so well, i.e. in ways that unlock economic growth, ensure national security and promote pro-social outcomes 🧩 Methodological pluralism. Improving social outcomes requires technical innovation, robust governance and structural reform. Sociotechnical pragmatists combine quantitative and qualitative approaches to amass evidence of what works in different contexts   Our article ties into an ongoing discourse around technology and social change. We welcome all feedback! Many thanks to Arvind Narayanan, Hannah Rose Kirk, Andrew Strait, Ralph Schroeder, and Magnus Boman for helpful comments on earlier versions of this article.