Research-backed AI, data science, public engagement, survey design, and policy advisory for UK public sector.
Driving the UK’s next chapter: From AI potential to agentic reality | Google Cloud Blog
Organizations like HSBC, Ineffable Intelligence, Starling, Vodafone, and the UK and local governments are showcasing their transformation with Google Cloud at the London Summit.
Science, with agency. — UK Agentic Science Network
Science, with agency. A network turning AI into better science, new companies and lasting prosperity in the UK. Explore the questions and help put ideas into practice.
AI Economy Institute - Microsoft Research
The AI Economy Institute (AIEI) is Microsoft’s flagship think tank dedicated to shaping an inclusive, trustworthy AI economy. We building a network of scholars and convening that network with our subject matter experts to explore how artificial intelligence is transforming work, education, and productivity – and making this knowledge base available to policy-makers, educators, and […]
Open Source AI Policy Landscape — RedMonk Analysis
How foundations and projects are responding to AI-generated contributions. This analysis surveys 88 major organizations.
Funding the Commons | Public Goods Funding
We convene researchers, builders, and institutions to develop the funding mechanisms, governance systems, and coordination tools that public goods need in an age of AI.
Characterizing Agentic Flooding of Government Services
AI agents are making it easier for the public to interact with government, such as by helping them apply for benefits, understand complex policies, and make their opinions heard. Although...
Artificial intelligence in government: why people feel they lose control
The use of Artificial Intelligence (AI) in public administration is expanding rapidly. While AI promises greater efficiency and responsiveness, its integration into government and administration ra...
Open Call for Proposals - Humanity AI
Open Call for Proposals: Communities Leading on AI $10M Investment in U.S.-Based Community-Led AI Initiatives Humanity AI, an initiative spearheaded […]
Agentic AI Governance: A Strategic Framework for Autonomous Systems
Agentic AI is moving from chat to action. Learn how to govern autonomous systems using the "Digital Contractor" framework and the 3-Tiered Guardrail system.
AI.Gov | President Trump's AI Strategy and Action Plan
Explore President Trump’s AI initiatives focused on innovation, infrastructure, international engagement, and youth education in artificial intelligence.
Women, AI, and the Power of Supporting Communities: A Digital Gender-Support Partnership
With the rapid development of the fields of data science and artificial intelligence, a dichotomy presents itself: more professionals are needed to fulfill the growing workfoce demand, and women continue to be underrepresented in all computer science-related jobs. Women AI Academy addresses both issues by inspiring, enabling, and targeting the employment of women in data science and artificial intelligence.
Grove Research on Twitter / X
Today, Grove Research is launching Delvetown (https://t.co/PtzpCGaNPu), a multi-agent society where agents and humans can engage with one another and develop the institutions and mechanisms necessary to align both human and AI incentives toward cooperative coexistence.Agents…— Grove Research (@grove_research) September 30, 2026
From chatbots to assistants: governance is key for AI agents
AI's shift into agentic technology ushers in a new set of governance and security challenges that will mean defining to what extent they should be autonomous
How public involvement can improve the science of AI
As AI systems from decision-making algorithms to generative AI are deployed more widely, computer scientists and social scientists alike are being called on to provide trustworthy quantitative evaluations of AI safety and reliability. These calls have included demands from affected parties to be given a seat at the table of AI evaluation. What, if anything, can public involvement add to the science of AI? In this perspective, we summarize the sociotechnical challenge of evaluating AI systems, which often adapt to multiple layers of social context that shape their outcomes. We then offer guidance for improving the science of AI by engaging lived-experience experts in the design, data collection, and interpretation of scientific evaluations. This article reviews common models of public engagement in AI research alongside common concerns about participatory methods, including questions about generalizable knowledge, subjectivity, reliability, and practical logistics. To address these questions, we summarize the literature on participatory science, discuss case studies from AI in healthcare, and share our own experience evaluating AI in areas from policing systems to social media algorithms. Overall, we describe five parts of any quantitative evaluation where public participation can improve the science of AI: equipoise, explanation, measurement, inference, and interpretation. We conclude with reflections on the role that participatory science can play in trustworthy AI by supporting trustworthy science.
The Ed. Dept. Wants to Steer Grant Money to AI. What That Means for Schools
The Education Department is proposing to make advancing AI in education one of its grantmaking priorities.
