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

• URL: http://arxiv.org/abs/2005.07572v3
• Date: Fri, 22 May 2020 13:57:28 GMT
• Title: Participatory Problem Formulation for Fairer Machine Learning Through Community Based System Dynamics
• Authors: Donald Martin Jr. (1), Vinodkumar Prabhakaran (1), Jill Kuhlberg (2), Andrew Smart (1), William S. Isaac (3) ((1) Google (2) System Stars (3) DeepMind)
• Abstract summary: 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. Current practice neither accounts for the dynamic complexity of high-stakes domains nor incorporates the perspectives of vulnerable stakeholders. 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.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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