AI systems encode power: How algorithmic maps exclude marginalised knowledge and reshape societal reality
Original framing: “Why AI output is always a map, never a territory” — bing news
Marginalised communities—particularly indigenous peoples, Global South populations, and gig workers—are disproportionately affected by AI’s 'map-territory' problem. Their knowledge is mined for training data but excluded from system design, as seen in cases like facial recognition bias against darker-skinned people. Grassroots movements like *Data for Black Lives* and *Algorithmic Justice League* demand accountability, but their voices are often sidelined in mainstream tech discourse. Centring these perspectives reveals AI as a tool of structural violence, where 'maps' justify exclusion and exploitation.
AI’s 'map-territory' problem is not a technical glitch but a manifestation of deeper epistemic violence, where algorithmic systems encode colonial, capitalist, and patriarchal logics.
The technology’s outputs are shaped by training data sourced from marginalised communities but controlled by Silicon Valley elites, reproducing historical patterns of extraction seen in cartography, craniometry, and surveillance capitalism. Indigenous scholars like *Linda Tuhiwai Smith* and *Miriam Posner* have long warned that AI’s 'neutrality' is a myth, as it systematically excludes non-Western ways of knowing. Future solutions must centre decolonial design, participatory governance, and epistemic pluralism—shifting AI from a tool of control to one of collective liberation. Without this, AI will continue to produce 'maps' that justify exploitation, erasure, and inequality under the guise of objectivity.