Guiding Principles: How we actually work

Pillar 1: Research through practice

How knowledge is generated, tested, and learned in real-world contexts

• Research grounded in practice, with real-world consequences and impact
  

  AI-BRIDGES treats practice as a site of inquiry and responsibility. Research questions, methods and outcomes are shaped by real institutional and community contexts, with attention to how the work affects people, infrastructures, and decisions beyond academia.

• Building on prior work and collective experience

  The project builds on existing research, tools, infrastructures, and community knowledge, learning from past successes and failures before proposing new approaches. Innovation is pursued through extension, adaptation and alignment, rather than reinvention.

• Learning through iterative practice, in real contexts
  

  Learning emerges through building, testing and iterating on workflows, tools and practices, in real-world settings, and is supported by ongoing reflection and adjustment, rather than abstract design alone.

Pillar 2: Holistic and inclusive data practices

How data work is framed, scoped and designed across contexts

• Holistic approaches to data work
  

  AI-BRIDGES approaches data work as a full lifecycle, from preparation and contribution to querying, analysis, reuse and impact. Individual workflows or tools cannot stand alone; sustainable practice requires attention to how data moves across systems and roles over time.

• Global and multilingual by design
  

  The project works across languages, regions, and institutional contexts, including those often overlooked, and treats multilingualism and contextual diversity as foundational design considerations rather than add-ons.

• Openness with care
  

  Open practices are pursued with attention to ethical, cultural, and institutional constraints. Decisions about participation, data sharing, and reuse recognize that not all data or knowledge can or should be fully open.

Pillar 3: Collaboration via partnerships and participation

How institutions, communities and contributors work together

• Working across stakeholders, together
  

  AI-BRIDGES engages multiple stakeholder groups simultaneously, including institutions, open knowledge communities, technologists, researchers, and funders. Progress depends on coordination and shared sense-making across these perspectives.

• Institutions as partners, not data sources
  

  Institutions are engaged as active partners in shaping the work, with attention to sustained collaboration, shared decision-making, and ongoing contribution rather than one-off data extraction.

• Flexible and mindful participation
  

  Participation is intentionally flexible, allowing contributors to engage in different ways and at different levels, depending on their interests, capacities, and contexts, while maintaining transparency, care, and mutual respect.

Pillar 4: Knowledge sustainability and stewardship

How responsibility for knowledge extends beyond individual projects

• Contributing back to the commons
  

  AI-BRIDGES treats contribution back to Open Knowledge and Open Data infrastructures as an integral part of the work, sharing outputs, documentation, and insights into communal spaces to support reuse and collective benefit.

• Designing for sustainability beyond the project
  

  The project is designed with the understanding that funding is time-bound, while the infrastructures, practices, and communities it engages with are not. Outputs are developed for durability and reuse beyond the project’s funded period.

• Open Knowledge governance as an ethical public interest choice
  

  In a landscape increasingly shaped by proprietary AI systems, AI-BRIDGES deliberately centers community-governed Open Knowledge and Open Data as foundations for public-interest AI.

Contact us for more details: [email protected]