Participation Is not a Design Fix for Machine Learning

Abstract

This paper critiques popular modes of participation in design practice and machine learning. It examines three existing kinds of participation in design practice and machine learning participation as work, participation as consultation, and as participation as justice – to argue that the machine learning community must become attuned to possibly exploitative and extractive forms of community involvement and shift away from the prerogatives of context independent scalability. Cautioning against “participation washing”, it argues that the notion of “participation” should be expanded to acknowledge more subtle, and possibly exploitative, forms of community involvement in participatory machine learning design. Specifically, it suggests that it is imperative to recognize design participation as work; to ensure that participation as consultation is context-specific; and that participation as justice must be genuine and long term. The paper argues that such a development can only be scaffolded by a new epistemology around design harms, including, but not limited to, in machine learning. To facilitate such a development, the paper suggests developing we argue that developing a cross-sectoral database of design participation failures that is cross-referenced with socio-structural dimensions and highlights “edge cases” that can and must be learned from.