Community Innovation Programs

🚀 Now publicly available 🚀 The Data Innovation Toolkit! And Repository! (✍️ coauthored with Maria Claudia Bodino, Nathan da Silva Carvalho, Marcelo Cogo, and Arianna Dafne Fini Storchi, and commissioned by the Digital Innovation Lab (iLab) of DG DIGIT at the European Commission) 👉 Despite the growing awareness about the value of data to address societal issues, the excitement around AI, and the potential for transformative insights, many organizations struggle to translate data into actionable strategies and meaningful innovations. 🔹 How can those working in the public interest better leverage data for the public good? 🔹 What practical resources can help navigate data innovation challenges? To bridge these gaps, we developed a practical and easy-to-use toolkit designed to support decision makers and public leaders managing data-driven initiatives. 🛠️ What’s inside the first version of the Digital Innovation Toolkit (105 pages)? 👉A repository of educational materials and best practices from the public sector, academia, NGOs, and think tanks. 👉 Practical resources to enhance data innovation efforts, including: ✅Checklists to ensure key aspects of data initiatives are properly assessed. ✅Interactive exercises to engage teams and build essential data skills. ✅Canvas models for structured planning and brainstorming. ✅Workshop templates to facilitate collaboration, ideation, and problem-solving. 🔍 How was the toolkit developed? 📚 Repository: Curated literature review and a user-friendly interface for easy access. 🎤 Interviews & Workshops: Direct engagement with public sector professionals to refine relevance. 🚀 Minimum Viable Product (MVP): Iterative development of an initial set of tools. 🧪 Usability Tests & Pilots: Ensuring functionality and user-friendliness. This is just the beginning! We’re excited to continue refining and expanding this toolkit to support data innovation across public administrations. 🔗 Check it out and let us know your thoughts: 💻 Data Innovation Toolkit: https://lnkd.in/e68kqmZn 💻 Data Innovation Repository: https://lnkd.in/eU-vZqdC #DataInnovation #PublicSector #DigitalTransformation #OpenData #AIforGood #GovTech #DataForPublicGood

Delivering Abundance from the Ground Up Abundance, the remarkable book co-authored by Ezra Klein and Derek Thompson, is rightfully a blockbuster sensation. The central thesis of the book — that it is time for the nation to build again and embrace abundance over scarcity — is timely and compelling. In our latest newsletter, Michael Saadine. Colin Higgins and I lay out the reality that delivering abundance, during a period of federal scale back and volatility, has been, ipso facto, delegated to state and local governments and their private and civic allies. We pose three challenges for public, private and civic leaders to tackle. First, what kind of public institutions are needed to deliver Abundance? Our view, along with close colleagues like Joshua Humphries, is that many public agencies and authorities have more power and access to capital than they think. Unlocking those hidden assets will be critical. Second, how will innovative practices be diffused and scaled across our federal republic? The U.S. never lacks for first movers — one city or county or state innovating on housing production or transportation expansion or energy modernization. But to move an innovation meaningfully from 1 place to 50 or 100 or 1000 places needs to be sorted. Here is where routinization, dissemination and differentiation (across different kinds of innovations) must be perfected. Finally, what kind of multi-sectoral collaboration needs to be created and amplified? From the founding of the Republic, problem solving in the U.S. has always been driven by networks of public, private and civic institutions and leaders (what we call New Localism) rather than the public sector alone. Unleashing the full energies and expertise of the nation must be brought to bear. Localizing the delivery of Abundance, particularly given the fragmentation of municipalities and states, will not be simple. But it is the path to getting things done in this nation. https://lnkd.in/ea-Bxvqe

Can we stop saying research has been co produced with communities when the only engagement was a one hour session at the end for a £27.50 voucher? Because that is not co production. Words matter in research. When we blur the lines between consultation, PPIE and true co production, we are not just mislabelling a process. We are misrepresenting communities, their labour and their expertise. Co production means communities shaping the work from the beginning. It means being involved in defining the questions, shaping the design, influencing the methods and helping interpret what the findings actually mean. It means power is shared, not borrowed for an hour. Consultation is different. PPIE is different. Both are valuable and necessary parts of research. But they are not the same as co production, and pretending they are risks eroding trust with the very communities research claims to serve. Many of the communities I work with have experienced extractive research for decades. They have been asked to share their stories, their pain and their time, often for very little in return. When institutions then publish papers saying the work was “co produced”, it can feel like another form of erasure. Honesty matters here. If a project consulted people at the end, say that. If a project included a PPIE group that reviewed materials, say that. If a community genuinely shaped the research from start to finish, then absolutely call it co production. None of these approaches are lesser. They are simply different. But integrity in how we describe them is critical if we want to rebuild trust between researchers and communities. Good PPIE is not a tick box exercise. Co production is not a buzzword. & communities deserve more than being written into a methodology section after the work is already done. If we want research to be equitable, inclusive and impactful, we need to start by being honest about how we do it. ----- Hey, I’m Neelam, CEO and Founder of Cysters, est 11 years ago. We specialise in community research, health inequalities and justice based, intersectional care, working alongside communities to shape systems that truly listen and respond. Follow me, connect with me, or catch me over a hot chocolate to explore how we can work together ❤️

Necessity fuels innovation and determination. We saw this truth in action in Parsa, a remote village in Northern India. There, families relied on costly and unreliable diesel generators. The need for a better solution drove our team to design state-of-the-art mini-grids tailored to the community’s needs. The journey wasn’t easy. Challenges arose and even with the progress, not every grid was a success. Especially early on, geography was destiny: Some villages were bad fits for the mini-grids because of their demand or their layout. But with technical expertise and strong partnerships, the team persisted—and succeeded. No longer were the traditional government grid model or a string of diesel generators the only ways to power a village. The mini-grid’s power was competitively priced, eventually around $0.25 per kilowatt hour, and much more reliable than the state grid and far cheaper than diesel. Walking through Parsa, I saw the transformation firsthand. Reliable, affordable energy was powering homes, businesses, and livelihoods. This is proof that bold solutions create opportunity and lasting impact.

India’s innovation challenge may not be about ideas - it may be about scale. Many technologies follow a similar journey: Discovery → Prototype → Validation → Scale-up → Deployment India is strong in the early stages of discovery and prototype development. But many promising technologies struggle during the transition to scale-up. This stage typically requires: • Pilot-scale infrastructure • Testing and certification facilities • Process engineering capability • Industry partnerships Without these, strong ideas can remain stuck at the prototype stage. This becomes visible through the technology compendiums released by Government department like #DSIR, Indian research & academic institutions such as #CSIR (Council of Scientific and Industrial Research), #NRDC (National Research Development Corporation), Indian Institutes of Technology, Medical research institutes, and other laboratories showcasing hundreds of promising indigenous technologies. Yet the translation to industry remains limited. Available patent working disclosures suggest that only about 2–6% of patents in India show evidence of commercial use, highlighting the challenge of moving from invention to large-scale deployment. I often think of this as the “#ScaleupGap” in the innovation ecosystem, the space between laboratory success and industrial impact. Strengthening this “#MissingMiddle” could significantly accelerate: • Technology commercialization • Startup growth • MSME innovation • Industrial competitiveness India has the talent and ideas. Designing stronger pathways for #scaleup may be the next big step we need. 💬 Where do you think the biggest bottleneck lies when moving from prototype to commercialization? Image Courtesy: AI

#BackwardInduction for Innovation: Securing Space for #Drones in the #PublicGood Domain In a world increasingly driven by technological innovation, drones hold immense promise in addressing critical public good challenges like #healthcare delivery, #disasterresponse, and environmental #conservation. Yet, despite their transformative potential, these innovations often find themselves competing for limited funding with other emerging technologies. The answer probably lies in Backward Induction, a problem-solving approach where we start with the desired outcome and work backward to determine the necessary steps to achieve it. Backward Induction in Action for Drones: 🛩️ Backward induction begins with a clear vision: "Drones as indispensable tools in public service." From this endpoint, we work backward to identify what must be true at each stage for this vision to become reality. For drones, this may mean asking: - What must success look like? Fully operational drone corridors, widespread adoption in healthcare and disaster relief, and clear public trust in their impact. - What is required for that success? Inclusive regulations, scalable infrastructure, skilled local operators, and demonstrated benefits through pilot projects. - What steps lead us there? Engaging stakeholders, addressing ethical concerns, developing training programs, and creating supportive policies. Why Backward Induction is Critical? Because Emerging Technologies often face resistance, whether due to lack of awareness, inadequate infrastructure, or competing priorities. Backward induction forces us to anticipate these challenges, breaking the journey into manageable, actionable steps that remove barriers and build momentum. For example: - If the end goal is widespread use of drones for medical deliveries, the starting question becomes: What infrastructure is necessary? This may lead to steps like building drone ports or establishing drone-specific air traffic control systems. If trust is a barrier, backward induction helps focus on pilot programs that showcase measurable impact, convincing stakeholders of the technology’s value. A structured roadmap for navigating the complexities of public good innovation: 1. Define Success Clearly: Establish a vision that aligns with societal goals and resonates with funders and stakeholders. 2. Identify Critical Milestones: Break down the journey into specific steps, from policy development to capacity building. 3. Address Barriers Proactively: Anticipate challenges, such as funding gaps or regulatory hurdles, and develop strategies to overcome them. 4. Iterate Along the Way: Regularly revisit and refine the steps as circumstances evolve. The journey starts at the end. Let’s reverse-engineer a future where technology uplifts humanity, one step at a time. Caerobotics | Global Alliance on Drones in Development

#AiDays2025 Round Table : #Community Sourcing for low resource languages In an era where AI is fast shaping the contours of our digital future, VISWAM.AI initiative stands as a timely and transformational one. Their mission to build community-sourced Large Language Models (LLMs), grounded in India’s rich linguistic and cultural diversity, is not just pioneering—it’s redefining how inclusive and ethical AI should be built. By anchoring their work in community participation, linguistic preservation, and ethical co-creation, Viswam.ai offers a people-first approach to AI—moving beyond data extraction to cultural stewardship. Their ambition to mobilize 1 lakh community interns to collect data from underrepresented geographies across India is both bold and brilliant. This isn’t just about building better AI—it’s about building equity, agency, and cultural resilience through AI. 1. Linguistic Equity by Design In India, where linguistic hegemony often privileges English and Hindi, AI systems risk reinforcing this imbalance. The solution? Intentional design. Allocate equal engineering and validation efforts to low-resource languages. Ethical AI must be built on informed consent, community ownership, and fair compensation—because data is not just input, it’s identity and heritage. 2. Decentralized Internship Model By decentralizing AI development, we bridge the urban-rural digital divide. This model should focus on: Capacity building through training in ethics and digital literacy Inclusivity by involving women, Dalit and Adivasi youth Localized platforms using mobile-first tools in native languages Partnerships with Swecha, local NGOs, and institutions serve as trust bridges to ensure mentorship and sustainability. 3. Tools for Low-Resource Languages Many Indian languages are oral-first, with complex dialects and sparse corpora. Community-driven solutions—like collecting voice datasets from folklore, and crowdsourcing annotation—are key. Elders, poets, and storytellers become linguistic technologists, preserving not just language but legacy. 4. Trust & Transparency Bias in AI is structural. To mitigate it: Include diverse dialects and accents in training Conduct bias testing and community validation Promote explainable AI with local language dashboards and storytelling What’s Next? A living white paper on ethics, governance, and technical guidelines A roadmap for the internship program, with toolkits and impact metrics Collaboration with literary and linguistic organizations to enrich model depth VISWAM.AI is planting seeds for an AI movement rooted in language justice, data sovereignty, and community wisdom. Let’s co-create systems that don’t just understand our languages—but respect our voices. DC* Chaitanya Chokkareddy Kiran Chandra Ramesh Loganathan Centific

What if your power came from your neighborhood, not a big utility miles away? That’s the idea behind Utility-Lite, and it’s starting to catch on, especially in Hawai‘i. Utility-Lite is a new model where smaller energy providers install and manage local solar, battery, and microgrid systems. Unlike traditional utilities that control generation, transmission, and billing, Utility-Lite groups focus only on what’s needed: powering homes and businesses directly, with less overhead and more flexibility. Hawai‘i is a natural fit. Each island runs its own grid, and importing fuel is expensive. Add in wildfires, storms, and long delays for grid upgrades, and communities are looking for faster, local solutions. Recent policy shifts like Act 197 are helping open the grid to non-utility players for the first time. Imagine a neighborhood that installs its own solar + battery systems, managed by a local nonprofit. They stay connected to the grid, but rely on it less, pay a fixed monthly rate, and keep energy dollars in the community. Could Utility-Lite be the future for more places, especially those underserved or tired of waiting? Curious to hear your thoughts.

As a dyspraxic researcher, it would have been easy for me to rely on my own experiences when helping design the CODIM study. But no single person can represent an entire community. That’s why co-production matters. From the very beginning, we worked alongside a Lived Experience Advisory Group (LEAG) made up of dyspraxic adults who helped shape the language, accessibility, questionnaire design, recruitment materials and overall direction of the project. Their role wasn’t symbolic. It made the study better. Their insight challenged assumptions, highlighted gaps and helped ensure the research reflected a wider range of experiences than my own. We were also fortunate to have additional dyspraxic voices within the study team itself, helping strengthen the project even further. And co-production didn’t stop there. Conversations with the wider community, including feedback shared through social media, also helped shape our thinking and improve accessibility. For me, this is what good research looks like. Research for the community. Research with the community. Because lived experience is expertise. If you’re a dyspraxic adult living in the UK, we’d love to hear from you. 🔗 CODIM survey link https://lnkd.in/eMist256 #CODIM #Dyspraxia #HealthResearch #Neurodiversity #TheBlackDyspraxic

Extractive research does not feel like it when you're the one holding the syringe. It feels like methodology. Like rigour. Like fieldwork. It feels like genuinely caring about the people you're sitting across from, right up until the data is collected, the laptop bag is zipped, and the car back to the city is waiting outside. Then you cited their poverty. You didn't cite them. Their answers shaped your conclusions. Their stories built your argument. Their pain gave your findings weight. Their names appear nowhere. Your ethics board approved it. The community never did. Not really. They signed the form. That is not the same as having power over their own story. They answered your questions. That is not the same as shaping which questions got asked. They participated. That is not the same as leading. Here is how to adopt a different approach. One grounded in respect, shared power, and long-term connection. Build trust before collecting data → Start with relationships, not research agendas. Follow the community’s lead → Let local voices shape the questions that get asked, and the solutions that get prioritised. Use creative, inclusive methods → From story circles to role plays to community mapping, gather data in ways that feel natural and empowering. Co-analyse, co-write, co-present → This isn’t about handing over transcripts. It’s about sharing meaning-making power. ---- Want insights like this directly in your inbox? Sign up for my mailing list. It's FREE! 👉 https://lnkd.in/ec8mqV2M