Building and scaling AI algorithms that serve the most marginalized

02 · Approach

Depth
Rigor
Scale

We bridge rigorous methods with deep contextual understanding.

Building algorithms that help the most marginalized requires knowledge that lives in different places. Academic researchers bring the rigor to design methods that work. Grassroots organizations bring the contextual understanding of how systems function and which levers will actually move them. Governments bring the reach to act across an entire population. Transformative, scalable solutions come from bridging these worlds — and that bridging is the work ASCL is built to do.

03 · Premise

Systems Change

Lasting change for the world's poorest comes from the systems around them — courts, clinics, schools, local governments. More than 95% of what is spent serving the poor flows through these systems: from their own governments, from their own pockets, from local institutions.

A small improvement there compounds in ways no external program can match. So our work is to help these systems do what they already do, better.

Bicycles For The Mind

Algorithms are transformative, but to truly change systems, algorithms need to augment the people inside them - not replace them. That can mean helping a teacher understand where her students are stuck, a bureaucrat track why a vaccine hasn’t been delivered, or a villager follow where her local government is spending.

Taking inspiration from the MIT Bikeshop, we believe that AI should serve as bicycles for the mind — extending what people can do, never replacing them.



Born at the Massachusetts Institute of Technology and built by a community of alumni, students, and researchers at the Institute.