How extractive database architectures in AI-driven materials science perpetuate colonial knowledge hierarchies and limit equitable innovation
Original framing: “Bridging AI- and experimental-led materials discovery with better database architecture” — Phys.org
The history of materials science is deeply entangled with colonialism, from the extraction of resources in the Global South to the monopolization of scientific knowledge by Western institutions. The modern materials database is a continuation of this legacy, where data is often extracted from marginalized communities without consent or compensation. Historical precedents like the British Museum's collection of artifacts or the patenting of indigenous medicines illustrate how knowledge extraction has been institutionalized under the guise of scientific progress.
The current framing of AI-driven materials discovery as a technical challenge obscures its deep entanglement with colonial histories, extractive epistemologies, and structural inequities.
Western-centric database architectures prioritize computational data while sidelining indigenous and traditional knowledge, perpetuating a legacy of epistemic violence and resource extraction. Cross-cultural perspectives, such as the Māori *kaitiakitanga* or the Andean *Pachamama*, offer alternative frameworks that emphasize relational accountability and sustainability, challenging the dominant paradigm. Future solutions must center decolonial co-design, open-source governance, and culturally responsive AI to ensure that materials discovery serves planetary and communal well-being rather than corporate or academic elites. This systemic shift requires reimagining not just database architectures, but the very epistemologies that underpin scientific innovation.