To ensure that AI serves the many—not the few—we call on all creators, developers, institutions, and regulators to commit to the following actions.
1. Design for Cultural and Contextual Integrity
We advocate for the inclusion of minoritised languages, oral traditions, and Indigenous knowledge systems in AI training. Models must be locally relevant, linguistically inclusive, and culturally respectful. The loss of cultural nuance to fit into Western data structures is a form of erasure.
2. Eliminate Bias at Every Level
We actively identify and challenge biases in datasets, design, and deployment—especially those rooted in systemic injustice, colonial legacies, and intersecting forms of marginalisation. We must identify and address how race, gender, class, disability, neurodivergence, and geography intersect in AI harm.
3. Ensure Transparency and Disclosure
AI-generated content must always be clearly labelled. Users have a right to know what was generated, how it was made, and what data or models were involved. We also acknowledge AI’s limits. AI is probabilistic, not all-knowing. Systems must confess their gaps—especially when trained on incomplete or biased data—and allow users to question or challenge outputs.
4. Obtain Consent and Protect Rights
We prioritise informed consent, especially when referencing individuals, communities, or creative works. No training or fine-tuning should occur without explicit permission and rights verification. We reject systems that clone styles without credit or enable aesthetic imitation without permission. AI must empower creators, not undermine them.
5. Compensate Creators and Communities Fairly
We call for a global fund to ensure that creators whose work has contributed to AI systems are recognised and remunerated. Creative labour is not free.
6. Democratise Access
Equity begins with access. We must bridge the gap between free and enterprise-level models, and ensure people from underrepresented regions and backgrounds can build, test, and use AI without barriers.
7. Embed Accountability and Governance
AI policy and tool development must include those most affected by its impacts—especially artists, educators, students, and Global Minority communities. Participation must be designed into the process, not added after the fact. We support co-governance models that ensure AI systems remain aligned with plural societal values, and are open to community oversight, redress, and challenge.
8. Reimagine AI in Education and Learning
We demand safe, unbiased, and context-aware AI in education—tools that support critical thinking and local knowledge, not reinforce stereotypes or linguistic hierarchies. Automated assessments must not replicate stereotypes or funnel young learners into narrow futures.
9. Disclose and Reduce Environmental Impact
We commit to tracking and sharing the ecological cost of AI generation, and to promoting low-carbon, sustainable models and practices. We must resist extractive pipelines that treat data, people, and the planet as disposable. Ethical AI is sustainable AI.
10. Align with Justice, Legislation, and Collective Action
We align our work with emerging AI laws, global advocacy, and collective movements. This manifesto is part of a wider ecosystem working for ethical and accountable tech futures. We call for global coalitions, mutual learning, and radical transparency.
