Decolonizing AI: Lessons from Paola Ricaurte’s Powerful Conference on Computer-Supported Cooperative Work & Social Computing (CSCW) Keynote
While artificial intelligence (AI) is often heralded as the pinnacle of human innovation, its colonial underpinnings and ecological consequences demand urgent scrutiny. At the heart of the Computer-Supported Cooperative Work & Social Computing (CSCW) 2024 conference, the keynote address on ‘Building Feminist and Decolonial AI in Latin America: Experiences from the ground’ offered a powerful call to rethink how AI is conceptualized, developed, and deployed. Delivered by Paola Ricaurte who is associated with Tecnológico de Monterrey, and the Berkman Klein Center for Internet & Society, this thought-provoking session brought together insights from Indigenous knowledge systems, environmental justice movements, and critical AI studies to challenge the dominant paradigms driving the field today. Paola Ricaurte’s keynote delivered a clarion call to reimagine AI as a tool for collective liberation, grounded in Indigenous knowledge and environmental justice. What could we imagine for a post-colonial world alongside AI?Acknowledging the Colonial Roots of AI
The keynote began by situating AI within the broader context of colonialism and extractivism. Paola Ricaurte highlighted how the development of AI systems is often predicated on the exploitation of both human and natural resources:
Data as Extraction: The collection of vast datasets from individuals, often without their consent, mirrors colonial practices of resource extraction.
Environmental Costs: The energy-intensive processes required for training AI systems disproportionately impact communities already vulnerable to climate change, reinforcing global inequities.
By tracing these connections, the speaker emphasized the importance of situating AI within the histories of exploitation that continue to shape our world.
Centering Indigenous Knowledge Systems
One of the keynote’s most compelling arguments was the value of Indigenous knowledge systems in reshaping AI. Unlike Western approaches that often prioritize efficiency and scale, Indigenous worldviews emphasize interdependence, reciprocity, and sustainability. Paola Ricaurte offered examples of how these principles could inform AI development:
Designing AI systems that prioritize community well-being over corporate profit.
Embracing relationality by involving affected communities in decision-making processes.
Fostering sustainability by considering the long-term ecological and social impacts of AI technologies.
Resisting Technological Determinism
A recurring theme in the keynote was the need to resist technological determinism. Technological determinism is the belief that technological progress is an unstoppable force that inherently improves society—a view that the keynote critiqued for its oversimplification and erasure of harms. Paola Ricaurte critiqued this narrative, arguing that it obscures the harms caused by AI systems and limits our imagination of what technology could be.
The speaker called for a shift from "solutionism" to "relational design," emphasizing that AI should not be treated as a panacea for societal problems. Instead, AI must be embedded in broader movements for social and environmental justice, addressing root causes rather than symptoms. This stood out to me, as I do not assume that AI is a neutral tool, and I understand that we need to think beyond efficiency and towards justice. Anytime I come across the topic of relationality, I think that it makes so much sense, and shows how nonsensical things are that do not recognize the relational nature of how things work. In the context of AI and colonialism, relationality emphasizes the interconnectedness, reciprocity, and sustainability that are often central to Indigenous knowledge systems.
Relationality involves recognizing the relationships between humans, technology, and the environment, and it calls for AI development to prioritize community well-being and consider the long-term ecological and social impacts of AI technologies. We need to shift our thinking to considering technologies of resistance: Developing and supporting technologies that empower individuals and communities to resist the undesired use of AI in their lives and local contexts.
Practical Pathways Toward Decolonizing AI
The keynote concluded with actionable steps for decolonizing AI, offering a roadmap for scholars, practitioners, and policymakers. Firstly, there needs to be prioritization on the redistribution of power. Control over AI development must be shifted from corporations to communities, ensuring that those most impacted by AI systems have a voice in shaping them. Second, data sovereignty must be promoted. The rights of individuals and communities must be upheld in order to control how their data is collected, stored, and used. Third, pluriversal approaches must be adopted. We must move beyond one-size-fits-all solutions by embracing diverse ways of knowing and being. And finally, environmental harm must be mitigated. We must prioritize the development of energy-efficient AI systems and advocate for the responsible sourcing of materials used in AI infrastructure.
A Call to Action
Paola Ricaurte’s address was more than an academic critique; it was a rallying cry for transformative change. By decolonizing AI, we can move toward a future where technology serves the collective good rather than perpetuating harm. This requires courage, creativity, and a willingness to imagine alternatives to the status quo.
As the keynote concluded, the audience was left with a profound challenge: How can we, as a global community, reimagine AI in ways that honor human dignity and planetary health? How does this radiate outwards beyond the effects of AI and new technologies to society more broadly? The answers to this question will shape not only the future of AI but also the future of our interconnected world.