As part of the AI for the Global Majority initiative, a research team led by Denise Kasparian is exploring an alternative vision:
AI not as a proprietary asset, but as a commons rooted in cooperative principles.

The project brings together a multidisciplinary team including Katarzyna Cieslik, Cecilia Muñoz Cancela, Julieta Grasas, Hernán Gigena, and Agustina Sunico.

Beyond “collaborative AI”

The concept of Cooperative AI is often misunderstood.

In mainstream discussions, it is frequently used to describe systems that collaborate with humans or optimise cooperation between agents. But for this research team, the term has a very different meaning.

It refers instead to a long-standing political and economic tradition:
The cooperative movement, grounded in democratic ownership, collective governance, and social benefit.

This shift in perspective transforms the question from how AI collaborates to who owns, controls, and benefits from AI systems.

Four dimensions of Cooperative AI

To structure this approach, the team identifies four key dimensions:

• cooperative data, where users retain control over how their data is collected and used, for instance through data trusts or cooperatives; 
• cooperative design, based on participatory processes that involve users in shaping technological systems; 
• infrastructure ownership, questioning the dominance of proprietary platforms in favour of open and collectively governed alternatives; 
• benefit distribution, ensuring that the value generated by AI is shared rather than concentrated. 

Together, these dimensions outline a fundamentally different model of technological development that prioritises collective wellbeing over extraction and profit maximisation.

Learning from existing practices

Rather than starting from abstract theory, the project draws on existing cooperative initiatives, particularly in Argentina.

By studying organisations such as platform cooperatives and technology co-ops, the team examines how alternative models of digital infrastructure and governance are already being developed in practice.

This approach highlights a key insight:
the knowledge needed to rethink AI may already exist outside the dominant centres of technological power.

A gap in the literature

The research also reveals a striking imbalance in current knowledge production.

While academic and policy discussions on AI are abundant, much of the literature remains highly theoretical or speculative, with limited empirical grounding.

At the same time, research agendas are often shaped by Global North priorities, which may not reflect the realities or needs of actors in the Global South.

This creates a disconnect between how AI is discussed and how it is actually experienced.

A different starting point

By grounding its work in long-standing relationships with cooperative organisations, the team adopts a bottom-up research approach.

Rather than imposing predefined frameworks, it seeks to co-develop knowledge with actors who are directly engaged in building alternative technological systems.

This raises a broader question:
What would AI look like if it were designed not for markets, but for communities?

This is precisely where the team’s research turns next, an aspect we will explore in Part 2 of this series, coming soon.

About AI for the Global Majority

AI for the Global Majority (AI4GM) is a joint initiative of the Geneva Graduate Institute, Microsoft, and the International Telecommunication Union (ITU) dedicated to supporting innovative, evidence-based, and context-sensitive research on how artificial intelligence can benefit the world’s majority populations.

Bringing together interdisciplinary teams from across regions and sectors, the initiative explores practical pathways for more inclusive, responsible, and impactful AI in areas such as governance, education, health, finance, and digital innovation.

Selected teams will present their work in Geneva as part of the AI for Good Global Summit, contributing to international discussions on the future of AI and global development.