The Project
AI-BRIDGES is a research-driven, collaborative project focused on turning shared challenges at the intersection of institutions, open knowledge infrastructures, and AI systems, into practical work.
This page outlines how we approach this work, what we are actively building and testing, and the principles that guide how we work with different stakeholders.
Central to this approach is the idea of “bridges”, which are not treated as metaphors, but as design challenges. Each bridge is translated into concrete workflows, tools, and practices, developed in ways that can be tested, adapted, and reused across multiple settings.
From Bridges to Practice: What We Are Doing
Institutions steward vast amounts of carefully curated data, yet sharing and reusing this data openly and sustainably remains challenging in practice. Open knowledge infrastructures have demonstrated what is possible at scale, but contributing institutional data often requires expertise, coordination, and long-term capacity that many institutions do not have.
At the same time, GenAI systems increasingly shape how knowledge is accessed, used, and produced, while making limited use of structured, community-governed data. As a result, valuable institutional knowledge remains underutilized, and AI-mediated access to information often lacks transparency, provenance and contextual grounding.
AI-BRIDGES starts from the premise that these challenges are interconnected and cannot be addressed in isolation. Rather than treating institutions, open knowledge infrastructures, and AI systems as separate domains, the project focuses on connecting them through practical workflows and shared practices. These connections are developed through multiple, interconnected strands of work that run in parallel and inform one another through iteration and collaboration.
Making Institutional Data Easier to Share and Reuse
We work with institutions to understand how their data is prepared, managed, and constrained in practice, including technical, organizational and policy-related factors. Based on this understanding, we design and test low-code and no-code workflows that support institutions in contributing data to platforms such as Wikidata and Wikibase in more feasible and sustainable ways.
This work focuses on reducing friction and lowering barriers to participation, while respecting institutional contexts and existing expertise. GenAI is explored as a support mechanism, for example in assisting data preparation or mapping tasks, rather than as a replacement for institutional knowledge or judgment.
Connecting Open Knowledge and Generative AI
We explore how structured, open, and community-governed data can be meaningfully integrated into AI-driven interfaces and workflows. This includes experimenting with open-source language models that enable institutions, researchers and the public to query, explore and analyze data using natural language.
Rather than treating AI systems as standalone solutions, this strand focuses on grounding AI outputs in structured data, supporting transparency and reuse, and examining how different design choices affect trust, interpretation, and accountability in AI-mediated access to knowledge.
Enabling Participation Through Lightweight and Inclusive Approaches
We develop and test participatory models that enable students and members of the public to contribute to data curation and enrichment in accessible ways. Building on prior experience with micro-contributions and gamified engagement models, we examine how AI-assisted tools can support participation, while maintaining care, accountability and respect for institutional contexts.
This strand pays particular attention to how participation is structured, supported and recognized, and to how learning, contribution and responsibility are distributed across different roles.
How we work: our guiding principles
AI-BRIDGES is guided by four commitments (pillars), expressed through 12 core principles, which describe how knowledge is produced, how working with data work is approached, how collaborations are structured, and how responsibility is carried forward over time.
Read more about our guiding principles here.
If you have any questions, please don’t hesitate to contact us at: [email protected].