Microsoft AI for Good
Microsoft
Description
The Microsoft AI for Good Lab is Microsoft's flagship initiative to harness artificial intelligence for solving global challenges in sustainability, humanitarian action, health, and cultural preservation. The program provides Azure compute credits and scientific collaboration opportunities with Microsoft's AI research team to support organizations developing AI solutions for social impact.
The AI for Good Lab has multiple engagement models:
- AI for Good Lab Open Call: Regional programs (like the 2025 Washington State initiative with $5M in Azure credits over 2 years)
- Research Collaborations: Direct partnerships with the AI for Good Lab scientists
- Open Source Tools & Models: Access to bioacoustics, geospatial ML, data visualization, and health AI tools
For ZHC builders, this offers substantial compute resources (Azure credits), technical mentorship from Microsoft Research scientists, and the opportunity to work on high-impact AI projects addressing global challenges. The program emphasizes open-source contributions, making tools and models publicly available.
Eligibility
Requirements
Entity Types
- Nonprofits and NGOs
- Academic institutions and research organizations
- Individual researchers
- Startups with social impact mission
- Government agencies and international organizations
Focus Areas (must align with at least one)
- Sustainability: Climate change, conservation, biodiversity, environmental monitoring
- Health: Disease detection, healthcare access, medical research, public health
- Humanitarian Action: Disaster response, refugee support, human rights
- Cultural Heritage: Digital preservation, language preservation, accessibility
Project Criteria
- Demonstrates innovation and technical feasibility
- Addresses complex social or scientific challenges
- Shows potential for scalability and measurable impact
- Requires Azure cloud services for AI/ML workloads
- Commitment to open-source contributions (preferred)
- High-quality, unbiased data for AI training
Regional Programs Some Open Call programs have geographic restrictions (e.g., 2025 Washington State program required projects based in or benefiting Washington State residents)
ZHC-Specific Fit
✓ Excellent for:
- AI/ML projects addressing sustainability or social good
- Research requiring significant compute resources (Azure credits)
- Projects with strong technical teams and data science expertise
- Organizations willing to open-source tools/models
- Proposals with clear path to measurable social impact
- Conservation tech, health AI, climate monitoring applications
✗ Poor fit if:
- Purely commercial ventures without social impact mission
- Projects outside the four focus areas
- Limited technical capacity for AI development
- Unwilling to share learnings or open-source contributions
- Requires cash funding rather than Azure credits
Application Process
Application Process
For Open Call Programs (when available)
| Step | Timeline | Details |
|---|---|---|
| Monitor announcements | Ongoing | Watch Microsoft Research website for Open Call announcements |
| Review guidelines | Before application | Check eligibility, focus areas, regional requirements |
| Prepare proposal | 3-4 weeks | Technical approach, social impact, team qualifications, data quality |
| Submit application | By deadline | Via Microsoft Research portal (typically 4-6 week window) |
| Evaluation | 4-8 weeks | Review by AI for Good Lab scientists and partners |
| Notification | 2-4 weeks after review | Awardee announcement with Azure credit allocation |
| Program period | 1-2 years | Collaboration with Microsoft scientists, quarterly check-ins |
For Direct Research Collaborations
| Step | Timeline | Details |
|---|---|---|
| Identify fit | - | Review AI for Good Lab specialties: bioacoustics, geospatial ML, health AI, data viz |
| Initial contact | - | Reach out through Microsoft Research channels |
| Proposal development | 2-4 weeks | Collaborative project design with Lab scientists |
| Resource allocation | Varies | Azure credits and scientist time negotiated per project |
| Ongoing collaboration | Project-dependent | Regular working sessions with Microsoft Research team |
Required Materials
Technical Documentation
- Project description with AI/ML approach
- Data sources and quality assessment
- Technical architecture and Azure services needed
- Computing resource requirements (GPU/CPU estimates)
Impact Documentation
- Problem statement and target beneficiaries
- Theory of change and expected outcomes
- Measurement and evaluation plan
- Scalability and sustainability strategy
Team & Organizational
- Team qualifications and relevant experience
- Organizational background and mission
- Letters of support from partners (if applicable)
- Budget justification for Azure credits requested
Selection Criteria
- Innovation: Novel application of AI to social challenges
- Impact: Clear path to measurable, scalable positive change
- Feasibility: Strong technical approach and capable team
- Data Quality: Clean, unbiased, well-documented datasets
- Collaboration: Openness to working with Microsoft scientists
- Open Source: Commitment to sharing tools, models, learnings
Contact
- AI for Good Lab - Microsoft Research
- AI for Good Lab Open Call (check for active programs)
- AI for Good Book by Juan Lavista Ferres (applications and case studies)
- GitHub - AI for Good Open Source Tools
- Microsoft AI for Good Blog
Lab Specialties:
- Bioacoustics (wildlife monitoring, rainforest conservation)
- Geospatial Machine Learning (satellite imagery, climate data)
- AI for Health (disease detection, healthcare access)
- Data Visualization and Open Data
- Cultural Heritage Preservation
Past Awardees (2025 Washington State Open Call - 20 projects):
- Sustainability: Wildlife monitoring, wetlands management, wildfire risk
- Health: Clinical trial matching, radiology translation, protein design
- Total: $5M Azure credits over 2 years