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AI Decolonial Manyfesto

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About

The AI Decolonial Manyfesto is a conceptual framework and collaborative provocation designed to reshape the future of artificial intelligence through a decolonial lens. Rather than functioning as a piece of software, it serves as a foundational knowledge system that challenges the current dominance of Western male voices, whiteness, and wealth in the tech industry. The project seeks to uncover and reinvent the cultural assumptions underlying AI language and development, acknowledging that intelligence and artificiality are loaded terms subject to deep-seated cultural biases. By centering non-Western ways of being and knowing, the Manyfesto aims to undo the colonial erasure often found in modern technological narratives. The framework was developed over a period of 16 months by a diverse team of two dozen experts spanning the fields of AI, social sciences, humanities, and human rights. It operates as an evolving document that rejects the standard Western-normative language of ethical AI, which the authors argue often fails to destabilize existing patterns of domination. Instead, it advocates for decolonial governance that recognizes how human destinies are intertwined and emphasizes the importance of lived experience as expertise. The initiative is intended to be the basis for future workshops, academic research, and policy interventions that move beyond mere diversity and inclusion to address material and epistemic injustices. This resource is primarily intended for AI researchers, technology policy makers, and community advocates who are looking for a more radical and inclusive approach to AI ethics. It is particularly valuable for those working within or alongside historically marginalized communities who wish to reclaim agency over their socio-technical futures. By focusing on the relational nature of humanity and the role of technology in those relationships, the Manyfesto provides a roadmap for those who wish to foster a decolonial imagination through art, storytelling, and sensitive experience rather than just algorithmic efficiency. What sets the AI Decolonial Manyfesto apart from other AI ethics guidelines is its refusal to seek a global consensus or provide half-measure principles that reinforce the status quo. It explicitly calls for reparations for past and present dispossession and rejects the divorce of material reality from immaterial feeling. It recognizes AI not just as a business or science, but as a knowledge system capable of creating algorithmic truths that can either reinforce domination or be reshaped to foster dignity. This emphasis on power asymmetries and territory makes it a distinct and foundational resource for anyone questioning hegemonic narratives in the digital age.

Pros & Cons

Developed by a multidisciplinary team of two dozen experts from diverse geographic and racial backgrounds.

Explicitly addresses power asymmetries and the material extractivism inherent in AI development.

Offers a bilingual platform in English and Spanish to facilitate global exchange and conversation.

Provides a clear call to action through public signatures and collaborative workshop opportunities.

Prioritizes lived experience and situated contexts over Western-centric governance structures.

Lacks a technical software component or API for direct integration into development pipelines.

The abstract and provocative nature of the text may be difficult for some organizations to operationalize.

Does not offer a traditional software product trial because it is a conceptual framework.

Some community groups may reject the term decolonial, a limitation acknowledged by the authors.

Use Cases

AI ethics researchers can utilize the framework to critique and redesign algorithmic systems that currently ignore non-Western knowledge systems.

Policy makers can apply the principles of decolonial governance to create regulations that account for material extractivism and epistemic injustice.

Community organizers can use the Manyfesto as a foundational text to demand reparations and agency in how AI technologies are deployed in their territories.

Platform
Web
Task
decolonial framing

Features

lived experience expertise integration

decolonial governance models

epistemic asymmetry analysis

policy foundation guidelines

workshop development framework

multidisciplinary expert authorship

bilingual english and spanish support

public signatory list

FAQs

How can I contribute to the AI Decolonial Manyfesto?

You can contribute by signing your name to the document through the provided online form or by contacting the team directly for potential collaborations. The authors explicitly invite exchanges and the development of new statements from people affected by AI.

Is the Manyfesto intended to be a final set of rules for AI?

No, the authors describe the Manyfesto as a provocation and an opening rather than a final consensus. It is intended to be the start of a dialogue and the basis for future workshops, papers, and policy changes.

Is the Manyfesto available in languages other than English?

Yes, the Manyfesto is currently available in both English and Spanish on its official website. This allows the framework to reach a broader audience, particularly in regions where coloniality is a present reality.

How was the Manyfesto developed?

The document was created by a diverse group of two dozen experts who met over 16 months during the pandemic. The team included specialists in social sciences, humanities, and human rights from various global regions and backgrounds.

How does this differ from standard 'Responsible AI' principles?

Unlike standard frameworks that focus on inclusivity within existing systems, this Manyfesto rejects half-measure ethics that fail to address power asymmetries. It focuses on decoloniality, which requires reparations for past material and epistemic injustices.

Pricing Plans

Open Access
Free Plan

Full access to the manifesto text

Bilingual language support

Public signature capability

Community collaboration contact

Resource for policy drafting

Conceptual workshop foundation

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