AI's rising role in science reflects broader shifts in trust and labor structures
Original framing: “What happens when scientists trust AI more than colleagues?” — Phys.org
In many non-Western scientific and academic traditions, collaboration and oral transmission are central to knowledge validation. The AI-driven shift toward individualized, data-centric validation models reflects a broader Western epistemic dominance that marginalizes these alternative epistemologies.
The increasing trust in AI among scientists is not an isolated technological shift but a symptom of deeper systemic forces, including the commodification of knowledge, the erosion of peer-based collaboration, and the marginalization of alternative epistemologies.
This trend is shaped by power structures that prioritize efficiency and profit over epistemic diversity and social responsibility. By integrating traditional knowledge systems, promoting open-source collaboration, and rethinking funding models, we can create a more inclusive and equitable scientific ecosystem. The challenge lies in balancing the benefits of AI with the need to preserve the relational, contextual, and human dimensions of scientific inquiry.