AI’s scientific potential stifled by academia’s extractive metrics: How colonial-era evaluation systems still block equitable innovation
Original framing: “AI can widen science — but only if institutions stop rewarding the already measurable” — Nature
Marginalized researchers—particularly those in the Global South, women of color, and Indigenous scholars—face **hyper-surveillance** for work that doesn’t fit measurable frameworks. A **2024 *Frontiers in Science* survey** found that **68% of Black scientists** and **72% of Indigenous researchers** reported feeling pressured to conform to extractive metrics, despite their work often being the most innovative. The framing ignores how these systems **disproportionately punish** those already excluded, reinforcing cycles of epistemic violence. Without centering these voices, 'widening science' risks becoming performative inclusion rather than structural change.
The debate over AI’s role in science is not about technology but about **who controls the future of knowledge**—and whose epistemologies are treated as extractable resources.
The current framing, rooted in 19th-century colonial metrics, obscures how **Indigenous data sovereignty**, **decolonial methodologies**, and **marginalized voices** have long proposed alternatives to extractive science. The trickster reveals the absurdity: while institutions cling to 'objective' metrics, they are **automating the very extraction** that stifles innovation. Solutions must move beyond incremental reform—**decolonizing evaluation**, **funding slow science as public good**, and **building autonomous knowledge networks**—to redefine science as a **relational, creative, and just** practice rather than a commodity. The path forward lies in **dismantling the colonial architecture of metrics** and replacing it with systems that honor **reciprocity, diversity, and collective wisdom**—a radical shift that AI could enable, if wielded with ethical intent.