HELIOS Lab, Stanford Medicine

AI is moving fast into biomedical research, but Indigenous Peoples, whose data, land, and communities are directly affected, are often not consulted in how these tools are designed and governed. That problem is what I joined the HELIOS Lab (Stanford Medicine) to work on in my first quarter at Stanford, through AI-READI, a consortium under the NIH Bridge2AI initiative. Our study focused on the enabling conditions for good research practices and the ways Indigenous governance can be strengthened.

I started by synthesizing peer-reviewed literature on AI governance, Indigenous data sovereignty, and community-engaged research practices, then built our interview protocol. Between me and the PhD candidate I worked with, we conducted 30 interviews with Indigenous scholars and Tribal community members on data sovereignty, relationship building, and how AI and machine learning are actually entering medical research. Data collection was slow and intentional, as we were constantly seeking to build trust with the colleagues and Tribal community members we engaged with.

In April 2026, we presented and validated our initial findings with the Tribal Council. Since then, my work has pivoted to building ready-to-ship tools born from those interviews, starting with a plain-language consent form translator that runs entirely on a local machine, so no community data ever leaves the room. In August 2026 we return to the Tribal Council’s Health Committee for community members to test the tool and shape its next iteration, and that feedback will also inform the white paper we’re preparing for publication. These tools are built using a set of design principles:

All outcomes of this research (a white paper, contract review tools, partnership screenings) are built squarely for the Tribe we’re working with. They have ultimate say of whether our work is published and shared with the world, corresponding with our core value of data sovereignty and strengthened governance.