  Recent research on algorithmic fairness has highlighted that the problem
formulation phase of ML system development can be a key source of bias that has
significant downstream impacts on ML system fairness outcomes. However, very
little attention has been paid to methods for improving the fairness efficacy
of this critical phase of ML system development. Current practice neither
accounts for the dynamic complexity of high-stakes domains nor incorporates the
perspectives of vulnerable stakeholders. In this paper we introduce community
based system dynamics (CBSD) as an approach to enable the participation of
typically excluded stakeholders in the problem formulation phase of the ML
system development process and facilitate the deep problem understanding
required to mitigate bias during this crucial stage.
