Participatory Approaches to Machine Learning

The designers of a machine learning (ML) system typically have far more power over the system than the individuals who are ultimately impacted by the system and its decisions. Recommender platforms shape the users’ preferences; the individuals classified by a model often do not have means to contest a decision; and the data required by supervised ML systems necessitates that the privacy and labour of many yield to the design choices of a few.

The fields of algorithmic fairness and human-centered ML often focus on centralized solutions, lending increasing power to system designers and operators, and less to users and affected populations. In response to the growing social-science critique of the power imbalance present in the research, design, and deployment of ML systems, we wish to consider a new set of technical formulations for the ML community on the subject of more democratic, cooperative, and participatory ML systems.

Our workshop aims to explore methods that, by design, enable and encourage the perspectives of those impacted by an ML system to shape the system and its decisions. By involving affected populations in shaping the goals of the overall system, we hope to move beyond just tools for enabling human participation and progress towards a redesign of power dynamics in ML systems.