By: Kathleen Sandusky
14 Nov, 2024
As the world grapples with the potential impacts of AI on labour markets, what are viable alternatives to designing and making decisions about data and AI so that these technologies will not be used to displace workers or devalue their labour? This is the central question at the heart of Data Communities for Inclusion (DCI), CIFAR’s first Solution Network.
DCI recently launched a toolkit of shared resources based on the experiences of the Self-Employed Women’s Association (SEWA), a federation of cooperatives owned by women workers in India’s informal economy.
“We’re trying to flip the narrative of AI technology as a panacea solution to productivity and economic well-being, determined top-down by the values and decision-making of the few who hold power,” says Revathi Kollegala, a DCI member who is the Technology Mentor at SEWA Cooperative Federation, a federation of worker cooperatives owned and operated by women in India. “Instead, we’re working with our partners to explore how the governance and organizing practices of grassroots communities and cooperatives globally can point us to a completely different framework for how to design and make decisions about AI: one that is more equitable and democratic, and centering the value of workers’ rights and well-being.”
DCI’s website, developed with CIFAR’s support, is key to the network’s strategy. The site operates as an open-access and collaborative resource hub. It features a “Community Toolkit” that shares resources, guides and case studies demonstrating how long-standing principles of cooperative governance can be applied to the design and use of artificial intelligence in diverse applications across industries and practices.
One of the resources shared on the site is a report co-authored by Network member Salonie Muralidhara Hiriyur, outlining how Farmer Facilitation Centres (FFCs), operated in partnership by two women-owned farmer cooperatives in India, helped to ease some of the disruption caused by the COVID-19 pandemic. “Worker-owned collectives had already proven to be robust systems of governance and enterprise that had formed to alleviate some of the challenges and risks associated with agriculture,” comments Hiriyur. “By incorporating new technologies such as WhatsApp to the existing hyper-local systems of communication and information-sharing provided by the Centres, the collectives were able to lessen some of the instability of the pandemic among their members. We think this can work as an example of innovations that are already hard-wired into worker collectives, and that can be extrapolated to the upheavals that we’re already seeing in the effects of AI on local economies.”
“It’s exciting to see that the women of SEWA and other cooperatives globally are taking the design and ownership of data technologies specifically to address the harms and exclusions that mainstream technologies would otherwise present to their worker-owners,” says Kollegala.
These opportunities are not limited to agriculture in India, argue the team behind DCI. They say that these models can be used by communities in virtually every country, in any industry — and that technology designers can learn from and apply the democratic organizing and decision-making practices of cooperatives to create more equitable AI.
“We’ve shared this open-access toolkit so that others can adapt and reuse it in their own contexts and communities, and we’d love to include their resources and approaches in it, too,” says Colin Clark, co-director of DCI, who is also a co-founder of Canada’s Lichen Community Systems worker cooperative. “When those who are most impacted by AI technologies are supported to lead and own the process of designing them and deciding how they should be used, we reduce the potential for systemic harms and open up the possibility of more equitable innovation by people who have historically been shut out of access to power.”
An important feature of DCI’s approach is the shift from traditional methods of engagement and consultation to community-led design, an emerging best governance practice for AI development and deployment.
“CIFAR’s guiding aim has always been to convene and mobilize the world’s most brilliant people across disciplines and at all career stages to advance transformative knowledge and solve humanity’s biggest problems, together,” comments Elissa Strome, the Executive Director of the Pan-Canadian AI Strategy at CIFAR. “The work of Data Communities for Inclusion is a great example of this, because it brings together people with tremendous technological expertise and people with deep understanding of the principles of collective decision-making, co-creation and inclusion. But perhaps most importantly, this group sets a new standard for collaborative and responsible AI deployment because of the meaningful participation of the primary users, whose domain knowledge and wisdom are invaluable to setting and achieving the collective aims. This is an exciting project and I’m looking forward to watching the DCI network continue to grow.”
This year, DCI has moved to a self-sustaining model, built on the infrastructure and tools supported through the three-year program funded by CIFAR through the Pan-Canadian AI Strategy. The team plans to grow the platform and tools through their collective work and ongoing knowledge sharing. “Key to this growth and knowledge sharing will be the input and contributions from people already engaged in collective organizing throughout the world,” says Clark. “We welcome anyone to reach out to us to share their ideas and experiences, by using the link from the site.”