Global DSI Network Charter

This is a draft

Unlocking the Power of Data & AI to Drive Social Impact Locally 

Overview and Mission Statement:

data.org envisions a world in which people everywhere can use data and AI to solve society’s greatest challenges and improve lives around the globe. To this end, we are a platform for partnerships to build the field of Data for Social Impact (DSI). Building the field of DSI is new and challenging. No one organization can do this alone. It needs plurality of partners. It requires an ecosystem, especially given our goal to train or engage one million purpose-driven data practitioners globally by 2032.  

The Global DSI Network is a community of practice where members can share resources, offer trainings, and aggregate market insights. We are committed to empowering individuals, universities, private sector enterprises with ESG programs, social impact organizations, governments, social entrepreneurs, etc.  with the knowledge, skills, and tools necessary to leverage data and AI effectively for positive societal change. The following charter outlines our guiding principles and values that bind all members of the network. Each member has to affirm that they will abide by the charter. 

Principles and Values of the DSI Charter: 

1. Localism and Community Involvement: 
   • Engage key local experts in co-creation and co-design of initiatives to ensure relevance and cultural sensitivity. 
   • Adopt context-relevant pedagogy that reflects community needs, priorities, and challenges. 
2. Interdisciplinarity and Translational Skills: 
   • Break siloes by engaging with cross-sector data and training that empowers participants with the knowledge, skills, and tools across domains such as climate, health and financial inclusion. 
   • Prioritize the entire data value chain, from collection to communication, to drive decisions.  Focus on research, writing, language skills, and cultural knowledge to effectively convey insights. 
   • Cover essential skills from humanities and social sciences, as much as technical skills in data science training curricula to create well-rounded purpose driven data practitioners
3. Sociotechnical Approach (& Human Systems): 
   • Place humans and communities at the center of program and learning design, with data and technology as enablers. 
   • Address societal-scale problems by considering both data systems and human systems. 
4. Demand-Generation (Experiential Learning): 
   • Expose learners to real-world use cases and challenges from local Social Impact Organizations (SIOs), community-led organizations, and government partners. 
   • Enable partnerships between academia and SIOs, government and community organizations to provide hands-on experience to deepen understanding and skills application for emerging talent. 
5. Inclusion, Diversity, Equity, Accessibility (IDEA) and Intersectionality: 
   • Embrace intersectionality to address diverse needs and perspectives. Acknowledge the digital divide and gender inequality while underscoring the infrastructural challenges across regions and existing policies. 
   • Incorporate design that is sensitive to the lived experience of historically marginalized communities and is responsive to their needs to ensure equitable participation of under-represented demographics. 

Code of Conduct  

1. Surface Ethical Considerations:  
   • Promote the ethical use of data and ensure that our endeavors adhere to principles of privacy, consent, fairness, transparency, and accountability in line with data protection regulations.  
   • Prioritize engagements that equip participants with the knowledge and skills needed to understand responsible data and AI governance. 
   • Treat all members and participants with respect, consideration, and patience, assuming good intent, but showing zero tolerance for bullying, harassment or discrimination of any kind
2. Promote Knowledge Sharing and Collaboration: 
   • Encourage each other to share resources, best practices, and lessons learned with one another and with the wider ecosystem openly. 
   • Facilitate collaborative projects or initiatives that leverage the collective expertise and resources of network members. 
3. Provide Support and Resources: 
   • Where possible, seek to offer support and resources to network members, such as training opportunities, funding opportunities, or technical assistance. 
   • Ensure that members have access to the tools and resources they need to effectively collaborate and achieve their goals. 
4. Celebrate Successes and Milestones: 
   • Acknowledge and celebrate successes and milestones achieved by each other. 
   • Recognize the contributions of individuals and organizations and highlight examples of successful collaboration within the network, giving appropriate credit to all involved, regardless of seniority or specialism 
5. Continuous Learning:  
   • Embrace a culture of continuous learning and improvement, regularly evaluating and updating programs based on participant feedback, emerging trends, and best practices in the field of data for impact. 
   • Have shared accountability by aligning on general principles and practices for Monitoring, Evaluation, Accountability and Learning (MEAL) framework to advance the sector overall.