5 Ways Cooperatives Can Shape the Future of AI
The development of artificial intelligence (AI) is currently dominated by a small group of large firms, including OpenAI, Alphabet, Amazon, Meta, and Microsoft. These companies leverage vast computational resources, proprietary datasets, and technical talent to control the landscape of AI. This concentration of power raises significant issues, including privacy violations, high environmental costs, and biases in AI models that can perpetuate discrimination in various sectors such as hiring, healthcare, and policing.
However, a different model is emerging—AI cooperatives. These organizations operate under cooperative principles and offer a promising alternative to the prevailing corporate AI model. They aim to democratize AI development and ensure it aligns with community needs and values.
What Cooperatives Can Do
Cooperatives have a history of managing complex systems for the collective good, from rural electrification to renewable energy. They follow seven core principles that translate democratic values into business practices, including:
- Voluntary membership
- Democratic control
- Member participation
- Autonomy
- Education
- Cooperation
- Concern for community
These principles enable cooperatives to expand stakeholder participation in AI development, challenge top-down governance models, and ensure profits are reinvested into the community.
Five Interventions to Shape AI’s Future
Cooperatives can influence the future of AI in five significant ways:
1. Democratizing Data Governance
AI cooperatives, though still emerging, demonstrate a new way to manage data governance. For instance, MIDATA, a Swiss nonprofit health-data cooperative, empowers members to control their medical information and decide who can access it. This model promotes fair and transparent research while safeguarding individual privacy.
2. Bringing Research to Civil Society
The AI discourse is often confined to elite circles. Cooperatives can bridge this gap by bringing AI discussions into local communities, making the technology more accessible and grounded in public needs. Organizations like Code for Africa are already working to democratize AI technology across the continent.
3. Advancing Education
Cooperatives can play a pivotal role in closing the AI knowledge gap. By developing multilingual AI platforms for cooperative education, they can empower members and ensure equitable access to AI learning resources.
4. Building Alternative Ownership Models
Ownership significantly shapes the direction of AI development. Unlike traditional corporate models, cooperatives prioritize long-term goals and democratic governance, which can lead to more ethical AI practices. For example, resource-rich cooperatives like IFFCO and Amul can leverage AI to enhance agricultural practices while maintaining community focus.
5. Critically Adapting AI for Cooperative Ends
Not all AI applications fit well within cooperative frameworks. However, where they do, cooperatives can lead the way in redesigning AI development to ensure accountability and community benefit. Initiatives like the Gamayyar African Tech Workers’ Cooperative in Kenya exemplify this approach by giving tech workers a stake in the platforms they help create.
A Model for What’s Possible
The READ-COOP initiative is a prime example of a successful cooperative in AI. Founded with EU funding, it has evolved into a self-sustaining cooperative that democratizes access to AI tools for historical document recognition. Members actively participate in shaping the platform, ensuring its development aligns with their needs rather than commercial interests.
Barriers to Scaling
Despite their potential, cooperatives face significant barriers in the AI landscape, including limited access to capital, technological resources, and policy influence. These challenges can lead cooperatives to rely on corporate APIs, undermining their mission. To overcome these obstacles, cooperatives must secure dedicated funding and develop robust advocacy strategies to influence AI governance actively.
Building a Broad Movement
For cooperatives to remain relevant in the face of AI’s rapid evolution, they must engage with broader social movements and build networks that promote collective ownership and democratic control. By collaborating with labor unions and advocacy groups, cooperatives can effectively challenge the status quo and shape the future of AI for the common good.
Ultimately, the cooperative model presents a viable pathway for creating a more equitable and transparent AI landscape. By prioritizing community needs and democratic governance, cooperatives can redefine the role of AI in society and ensure that it serves the public interest.
