Shaping of Generalized AL/ML Systems

This project is to establish a field school and a network of scholars to study the way AI/ML algorithms shape, and are shaped by, conditions of space, place, and embodiment. An interdisciplinary group of scholars working together brings professional attention to the way information technologies are changing the places we live, and the way people interact with algorithms to orient themselves within spatial computing interfaces. This project is of interest to designers, decision makers, educators and computer scientists. Through an open call for participation, the field school documents mediated experiences of space and place, critique normative spatial representations, and using design as a mode of spatial inquiry. Spatial data training ML/AI algorithms occurs in a broad range of functions, such as real-time mapping, route-planning, and collision detection in co-present human-computer collaborations. The outcomes of this initiative include curriculum materials, and conference presentations and peer-reviewed publications addressing cultural and material conditions embodied in training data for shaping the outcomes of generalized AL/ML systems. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.