A Thought Piece on Black Futures in Technology

Artificial intelligence is often described as a neutral tool system that processes information, recognizes patterns, and generates outputs from data. But that description only works if we ignore a deeper question what counts as knowledge in the first place, and who gets to define it?

AI is not just computational. It is epistemological. It participates in shaping what is legible as intelligence, what is treated as valid information, and what is dismissed as noise. In that sense, AI systems are not outside knowledge; they are part of how knowledge is organized.

This is where Afrocentricity becomes essential.

Afrocentricity, as developed by Molefi Kete Asante, is not simply a cultural perspective. It is a theory of knowledge that begins with a simple but disruptive claim: all knowledge comes from a center. That center is cultural, historical, and lived. It is never neutral, even when it is treated as if it is.

The importance of this idea is that it makes something visible that technological systems tend to hide. If every system of knowledge has a center, then the question is not whether a center exists but which center is shaping what we call intelligence today.

Artificial intelligence systems are built from data that comes from somewhere. That “somewhere” is made up of archives, institutions, digital platforms, historical records, and patterns of visibility that have never been evenly distributed. When those materials become training data, they don’t lose their history. They carry it into the system.

So when AI produces outputs that look neutral, what we are often seeing is a particular historical and cultural center operating under the appearance of universality.

Afrocentricity matters here because it gives us a way to name that structure instead of treating it as invisible.

It also shifts the question. Instead of asking only whether AI systems are biased, we begin to ask how they organize knowledge itself. What kinds of intelligence become easy to recognize? What kinds of knowledge become difficult to see? What forms of meaning get flattened when they are turned into data?

These are not small questions. They go to the heart of how future knowledge systems are being built.

Machine learning depends on abstraction. It takes complex, situated, relational realities and compresses them into patterns that can be computed. That process is powerful, but it is also transformative. Context gets reduced. History gets compressed. Meaning gets detached from the environments that produced it.

Afrocentric thought pushes back against the assumption that this kind of abstraction is complete or sufficient. In Afrocentricity, knowledge is not just information. It is tied to memory, community, history, and lived experience. Meaning does not survive separation from its context without change.

So the tension is clear: AI systems move toward abstraction and generalization, while Afrocentric epistemology insists on grounded, located meaning.

That tension matters for how we think about Black Futures and technology.

A lot of contemporary Black Futures and Afrofuturist scholarship is already working in this space, especially scholars like Reynaldo Anderson and others who extend Africana intellectual traditions into questions of technology, futurity, and speculative design. What this work shows is that Black Futures thinking is not separate from questions of knowledge. It is a continuation of them.

It asks a simple but far-reaching question: who gets to define what the future looks like when the systems building that future are also defining what counts as intelligence?

Seen this way, Afrocentricity is not an “add-on” to conversations about AI. It is one of the frameworks that helps us understand what is actually happening inside those systems.

Because if intelligence is being defined through systems that inherit uneven histories, then the future those systems produce will carry those histories forward—unless the underlying assumptions are challenged.

Afrocentricity is one way of doing that work. Not because it answers every question about technology, but because it insists on starting from something most technological discourse avoids: the idea that knowledge is always located, and that location shapes everything that follows.

That changes the conversation.
It moves us away from thinking of AI as something we simply use and toward thinking of it as actively organizing how knowledge itself is understood. And once you see that, the question is no longer whether Afrocentricity belongs in conversations about AI and Black Futures.

The question is what those conversations are missing when they are not there—and what gets left out when knowledge is left uncentered.

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This article was developed with AI-assisted research and editorial support.