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Google AI for Social Good is a grant and award program from Google, in partnership with Google. org, that funds academics and nonprofits developing machine learning-based technologies to improve people's lives, especially in underserved communities. Projects tackle social, humanitarian, and environmental challenges including agriculture, wildlife conservation, healthcare, and market access for smallholder farmers.
Each selected project team receives funding, technical contributions from Google, and access to computational resources. Academics are recognized as Impact Scholars for their contributions. Eligible applicants are nonprofits and academic researchers building AI-driven impact projects in areas such as climate, health, and economic development.
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AI for Social Good awards AI for social good awards The AI for Social Good awards, in partnership with Google. org , aim to help academics and nonprofits develop machine learning-based technologies that can improve people’s lives — especially in underserved communities. Through this program, we aim to bring nonprofits and academics together to collaborate on projects that tackle social, humanitarian and environmental challenges.
Each project team in the program receives funding, technical contributions from Google and access to computational resources. Academics in this program will be recognized as "Impact Scholars" for their contributions towards advancing research for social good.
Aaditeshwar Seth (Indian Institute of Technology, Delhi) and Centre for Collective Development (India) Building AI-based Market Intelligence Systems for Farmer Collectives Saket Anand (Indraprastha Institute of Information Technology Delhi) and M. S.
Swaminathan Research Foundation (India) High-resolution Satellite Imagery for Modeling the Impact of Aridification on Crop Production: Paddy Cultivation in the Cauvery Delta Long Tran-Thanh (University of Warwick) and AfriScout (Kenya) Incentive Engineering and Truthful Mechanisms for Grassland Quality and Local Market Price Estimation in Africa John Dickerson (University of Maryland, College Park) and Kheyti (India) What Should I Grow Today so I Make Money Tomorrow?
Using Social, Environmental, and Market Data to Support Small Farmers’ Crop Planning Godliver Owomugisha (Busitema University) and Papoli Community Development Foundation (Uganda) Adoption of smartphone agro-applications for field-based disease diagnosis and real-time feedback for smallholder farmers Daphney-Stavroula Zois (University at Albany, SUNY) and AGRI-WEB (Ghana) Towards Achieving Better Market Access for Smallholder Farmers Trevor Darrell (UC Berkeley) and Wildlife Conservation Society (Nigeria) Nataliya Tkachenko (University of Oxford) and Panthera (Nepal, India, Malaysia, Senegal) Applying AI to Data Challenges in Wildlife Conservation Poonam Goyal, Navneet Goyal (BITS Pilani) and Wildlife Conservation Society (India) Collating and analyzing multi-modal, multi-lingual data for countering wildlife crime Wei Guo (University of Tokyo), National Centre for Biological Sciences, Bangalore and Bioversity International (India) Exploring and Managing human-bee conflict in Asian cities using AI Amulya Yadav (Penn State University) and Dakshin Foundation (India) Harnessing Agent-Based Models to Mitigate Marine Conservation Conflict in the Andamans Gianluca Demartini (University of Queensland) and Wild Chimpanzee Foundation (Côte d’Ivoire, Liberia, Guinea) Human-in-the-loop Chimpanzee Identification Ashwin Srinivasan (BITS Pilani, Goa Campus) and Mara Elephant Project (Kenya) Human-in-the-Loop Labelling System for Elephant Identification and Tracking Fei Fang (Carnegie Mellon University) and World Wide Fund for Nature (India, Nepal, Thailand) Media Text Monitoring for Timely Conservation Actions Patrick McSharry (Carnegie Mellon University) and Institute for Global Environmental Strategies (Japan) Using AI to impute missing data in the SDG indicators and test causality Andrew Katumba and Joyce Nakatumba-Nabende (Makerere University) and Moja Global (Uganda) Using Machine Learning to Predict Deforestation Haifeng Xu (University of Virginia) and World Wildlife Fund (Lao PDR) Combating Poaching through Community Influence Vipul Arora (IIT Kanpur) and CARE India Solutions for Sustainable Development (India) AI-based Smart Assistant for Child Deliveries in Low Resource Areas Thanh H.
Nguyen (University of Oregon) and Arogya World (India) Artificial Intelligence for Health Promotion Intervention in India Himabindu Lakkaraju (Harvard University) and Living Goods (Kenya, Uganda) Augmenting Community Health Workers Efficacy using AI Ayan Mukhopadhyay (Vanderbilt University) HelpMum (Nigeria) Data-driven Vaccine Demand Forecasting and Health Interventions in Nigeria Conrad Tucker (Carnegie Mellon University) and Centre for Chronic Disease Control (India) Deep Learning for Oral Cancer Screening and Referral: A Feasibility Investigation Arunesh Sinha (Singapore Management University) and D-tree (Zanzibar) Incentive Design for Better Health Coverage U.
Deva Priyakumar (IIIT Hyderabad) and Lord's Education Health Society | Wadhwani Initiative for Sustainable Healthcare (India) Intelligent Assistive System for Home-Based Care Delivery Yiqun Xie (University of Maryland, College Park) and Aquaya Institute (Kenya) Leave No One Behind: Spatial AI Enabled Settlement Mapping to Enhance WASH Access for Vulnerable Populations Toby Walsh, Yang Song (UNSW Sydney) and Mothers2Mothers (South Africa) Predicting early exit from the m2m health program Malay Bhattacharyya (ISI Kolkata) and Molecular Diagnostics, Counseling, Care and Research Center (India) Systematic clinical intervention for minors affected by Duchenne muscular dystrophy Toby Walsh, Yang Song (UNSW Sydney) and Infoxchange (Australia) The Bootstrap Problem in Recommender Systems Amulya Yadav (Penn State University) and Jacaranda Health (Kenya) Using AI to Prevent the Risk of Maternal and Neonatal Deaths in Kenya Rayid Ghani (Carnegie Mellon University) and Energy Harvest Charitable Trust (India) Reducing Crop Burning to Improve Air Quality in India Pradeep Varakantham (SMU, Singapore) and Wildlife Conservation Trust (India) Predicting human-wildlife conflict Bo An (NTU, Singapore) and Ashoka Trust for Research in Ecology & The Environment (India) Improving dam and barrage water release Tavpritesh Sethi (IIIT, Delhi), Pradeep Varakantham (SMU, Singapore) and Swasti (India) Improving health information for high HIV/AIDS risk communities Balaraman Ravindran ( IIT, Madras) and ARMMAN (India) Predicting risks for expectant mothers Arunesh Sinha (SMU, Singapore) and Khushibaby (India) Improving consistency of healthcare information input Mitesh Khapra and Pratyush Kumar (AI4Bharat , IIT Madras) and Storyweaver (India) Supporting publishing of underserved Indian language content Our resources are available to everyone We regularly share datasets, tools and services with the broader scientific community to be used, shared, and built on.
Based on current listing details, eligibility includes: AI-driven impact projects. Startups building a sustainable and equitable future. Applicants should confirm final requirements in the official notice before submission.
Current published award information indicates Funding amounts vary based on project scope and sponsor guidance. Always verify allowable costs, matching requirements, and funding caps directly in the sponsor documentation.
The current target date is rolling deadlines or periodic funding windows. Build your timeline backwards from this date to cover registrations, approvals, attachments, and final submission checks.
Federal grant success rates typically range from 10-30%, varying by agency and program. Build a strong proposal with clear objectives, measurable outcomes, and a well-justified budget to improve your chances.
Requirements vary by sponsor, but typically include a project narrative, budget justification, organizational capability statement, and key personnel CVs. Check the official notice for the complete list of required attachments.
Yes — AI tools like Granted can help research funders, draft proposal sections, and check compliance. However, always review and customize AI-generated content to reflect your organization's unique strengths and the specific requirements of the solicitation.
Review timelines vary by funder. Federal agencies typically take 3-6 months from submission to award notification. Foundation grants may be faster, often 1-3 months. Check the program's timeline in the official solicitation for specific dates.
Many federal programs offer multi-year funding or allow competitive renewals. Check the official solicitation for continuation and renewal policies. Non-competing continuation applications are common for multi-year awards.
Past winners and funding trends for this program
Digital Cities' Innovation Accelerator Small Grant Program is sponsored by U.S. State Department's Bureau of Cyberspace and Digital Policy (CDP). These small grants activate the private sector to deliver novel and innovative solutions to civic challenges. Projects must address a sub-national public service or infrastructure need AND incorporate trusted U.S. digital based solutions, empowering municipalities to improve public service delivery.
This NOFO provides an opportunity to all FY 2018 NIST SBIR Phase I awardees to submit a Phase II application following completion of Phase I. This NOFO provides instructions for FY 2019 NIST SBIR Phase II application preparation and submission requirements. In Phase II, work from Phase I that exhibits potential for commercial application is further developed. Phase II is the R&D or prototype development phase. To apply for a Phase II award, each Phase I awardee will be required to submit a comprehensive application outlining the proposed research and a detailed plan to commercialize the final product. Each NIST Phase II award is for up to $400,000 and up to a 24-month period of performance. One year after completing the Phase II R&D activity, the awardee shall be required to report on its commercialization activities. Up to an additional $6,500 may be requested for Technical and Business Assistance (TABA); see Section 5.11 for more information about TABA. Funding Opportunity Number: 2019-NIST-SBIR-02. Assistance Listing: 11.620. Funding Instrument: CA. Category: ST. Award Amount: Up to $400K per award.
Research on Circular Economy, Smart Manufacturing, and Energy-Efficient Microelectronics is sponsored by U.S. Department of Energy (DOE) Advanced Materials & Manufacturing Technologies Office (AMMTO). This funding opportunity supports innovative technology R&D across the manufacturing sector with a focus on circular economy, smart manufacturing, and energy-efficient microelectronics. While the stated deadline for full applications has passed, AMMTO frequently issues similar solicitations, and this highlights a relevant area of interest for the DOE.
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