Critical Tools for Machine Learning: Situating, Figuring, Diffracting, Fabulating Machine Learning Systems Design

Abstract

This workshop draws on feminist and other critical methodologies to construct interdisciplinary interventions in the design of machine learning systems. Theoretical concepts of “figuration”, “situating/situated knowledge”, “critical fabulation/speculation,” and “diffraction” are explored through hands-on experimentation to imagine and design machine learning systems in a more situated, inclusive, contextualize and accountable way. Through this “theory turned practice” approach the workshop aims to address systemic socio-cultural biases and develop more socially responsible frameworks of design. The workshop provides space for building a network for future research on interdisciplinary machine learning systems design.