Decolonial AI: From Critique to Constructive Alternatives

The decolonial turn in AI ethics, is not merely a critique of existing systems but a positive research agenda: 'a radical reimagining of AI's purpose and potential, challenging its inherent biases and potential for exacerbating existing forms of oppression.'

Birhane's own constructive proposal, as stated in the conclusion of the Oxford Academic chapter, is that 'for any technologies to be liberatory, they need to emerge from the needs of communities, and be developed and controlled by the communities that birth them.' This is a substantive claim about the conditions for legitimate technology development: not just better representation in existing development pipelines, but genuine local control over the design, ownership, and governance of AI systems.

The decolonial AI ethics framework, integrates multiple theoretical traditions: critical race theory (to analyze how racial hierarchy is encoded in algorithms), postcolonial studies (to analyze the structural continuities between classical colonialism and digital colonialism), feminist technoscience (to analyze how gender and intersectional categories shape technology design and use), and Indigenous knowledge systems (to provide alternative epistemological frameworks that challenge the universalist pretensions of Western AI development).

Decolonial AI ethics scholarship identifies several specific principles: algorithmic transparency and accountability (making algorithms understandable and creating mechanisms for holding developers accountable); addressing power imbalances (challenging the concentration of power in a small number of large technology companies); promoting democratic governance of AI (ensuring that affected communities have meaningful input into AI design and deployment); and incorporating a 'broader set of perspectives' that extends beyond the homogeneous demographic profile of current AI development teams.

Scholars have added the concept of digital sovereignty: the argument that communities and nations have a right to control their own digital infrastructure, data, and governance, just as they have a right to political self-determination. This connects decolonial AI ethics to broader struggles for indigenous data sovereignty, African data governance frameworks (like the African Union's Data Policy Framework), and proposals for alternative digital infrastructure that is not dependent on Western corporate monopolies.

Birhane's work emphasizes the epistemic dimension: Birhane's work calls for 'normalising critical thinking on new technologies' rather than accepting the framing that AI systems are neutral technical tools delivering objective solutions. The assumption that Western-developed AI provides 'AI solutions to social problems' in African or other Global South contexts embeds a colonial epistemology: the assumption that the problems have been correctly identified, that the solutions are known, and that local knowledge is irrelevant or inferior to technical expertise.

The constructive challenge is significant: decolonial AI is not simply a matter of training algorithms on more diverse data or adding people of color to development teams, though both may be useful. It requires questioning the fundamental premises of how AI systems are conceived, the categories they use, the problems they are designed to solve, the interests they serve, and the communities they are accountable to.