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Recent research on algorithmic fairness has highlighted that the problemformulation phase of ML system development can be a key source of bias that hassignificant downstream impacts on ML system fairness outcomes. However, verylittle attention has been paid to methods for improving the fairness efficacyof this critical phase of ML system development. Current practice neitheraccounts for the dynamic complexity of high-stakes domains nor incorporates theperspectives of vulnerable stakeholders. In this paper we introduce communitybased system dynamics (CBSD) as an approach to enable the participation oftypically excluded stakeholders in the problem formulation phase of the MLsystem development process and facilitate the deep problem understandingrequired to mitigate bias during this crucial stage.

Participatory Problem Formulation for Fairer Machine Learning Through Community Based System Dynamics