Resistance Strategies: Capitalistic Narratives and Anti-Racist Imaginaries for AI Futures

“No level of individual self-actualisation alone can sustain the marginalised and oppressed.

We must be linked to collective struggle, to communities of resistance that move us outward, into the world."

Bell Hooks, 1993

• Research Disclaimer

  This document provides a summary of key ideas and insights from my research. It is not a comprehensive representation of the full study and may omit detailed analyses, methodologies, and supporting evidence for the sake of brevity. The findings and interpretations presented here have not undergone formal peer review and should be considered preliminary.

  Readers are encouraged to approach the content with this context in mind and to seek further clarification or discussion if needed. For inquiries about the complete research or for collaboration opportunities, feel free to reach out directly.

To understand capitalism, we must confront its roots in race and colonialism—not as footnotes, but as its very core. The same is true when we talk about artificial intelligence (AI). The systems we build today carry forward the biases of the past, reinforced by capitalism’s hunger for profit and power. Yet, history also offers us lessons of resistance and hope, showing how anti-racist movements can inspire a different path forward for AI development.

Let me take you on this journey, tracing how racial capitalism shapes AI and why embracing resistance narratives matters for the futures we imagine.

Capitalism, Race, and AI: A Historical Continuum

For centuries, capitalism has relied on race to justify exploitation. From the colonial project to today’s “data empires,” the story remains the same: control and extract from the marginalised while enriching the privileged. As socio-technical imaginaries—a society’s shared visions of science and technology—emerge, AI risks continuing this legacy.

Racial capitalism’s intersection with AI lies in the appropriation of data. Human lives are increasingly colonized by surveillance systems that monetise every trace we leave behind. It’s a continuation of the old story: bodies, resources, and territories once conquered for empire now find their modern equivalent in the extraction of data.

Intersectionality: A Lens for Understanding and Acting

Race isn’t the only axis of oppression. Patriarchy, religion, culture, and nationality—all have been wielded to maintain unequal systems. This is where intersectionality, born from Black feminist theory, becomes crucial. It teaches us that oppression operates across multiple dimensions and invites us to consider the unique experiences of those who live at the intersections.

For AI, this means rejecting one-size-fits-all approaches. Algorithms should not treat all individuals in a category as the same. For instance, Black women do not experience bias in the same way as Black men or white women. Intersectionality compels us to design systems that see and respect this complexity.

Yet, we must tread carefully. Intersectionality, like a house of mirrors, can reveal competing realities. Take the homelessness management system in Los Angeles. While it efficiently allocates resources, it does so by extracting intimate, often painful, personal data from unhoused individuals. These people, already vulnerable, are forced to consent to widespread sharing of their data, even with law enforcement. Efficiency, in this case, comes at the cost of dignity.

Lessons from Resistance

History is rich with movements that challenge the narratives of power. Black feminist activism in the 1960s and 70s gave us new language to confront systemic racism. More recently, the Black Lives Matter (BLM) movement leveraged digital platforms to scale grassroots resistance globally. And in Latin America, the ongoing migration crisis shows how identities and cultures are reshaped in the face of socioeconomic inequality.

These movements remind us that systemic change comes not from moments of disobedience but sustained movements. They offer blueprints for resisting oppressive systems, including those rooted in AI.

A Path Toward Decolonised AI

Resistance, as I see it, is a form of epistemic disobedience—a refusal to accept the "truths" imposed by colonial and capitalistic systems. For AI, this means rejecting practices that extract and exploit data for profit. It means imagining futures where AI empowers rather than oppresses.

My research then proposes three strategies for creating equitable AI systems:

1. Language as Collective Power
   Ensure inclusive communication that allows marginalised groups to lead conversations about AI ethics.

2. Building Movements, Not Moments
   Organise sustained efforts to disrupt exploitative data practices and imagine "unmarked spaces" free from data colonialism.

3. Decolonising Multiethnic Experiences
   Transform core systems that reinforce power imbalances, amplifying voices from diverse communities.

Conclusion: AI for Everyone, Not Just the Few

This vision for AI is not about rejecting technology but redefining its purpose. By debunking myths of capitalism and elevating resistance narratives, AI can foster fair representation, sustainability, and impactful outcomes. Transforming these systems is both a technical and ethical imperative, grounded in historical lessons and collective responsibility.

The path forward is uncertain, but it begins with the courage to imagine something different—and to act on that vision. Let’s learn from the past to create AI futures rooted in fairness, sustainability, and respect for all.