Knowing Machines is a research project tracing the histories, practices, and politics of how machine learning systems are trained to interpret the world.
We are developing critical methodologies and tools for understanding, analyzing, and investigating training datasets, and studying their role in the construction of “ground truth” for machine learning. Our research addresses how datasets index the world, make predictions, and structure knowledge cultures. Working with an international team, we aim to support the emerging field of critical data studies by contributing research, reading lists, research tools, and supporting communities of inquiry that are focused on the foundational epistemologies of machine learning.
Knowing Machines is sponsored by the Alfred P. Sloan Foundation.