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

TL;DR

This paper introduces community based system dynamics (CBSD) as a participatory approach to improve fairness in machine learning by involving vulnerable stakeholders in the problem formulation phase, addressing bias and complexity.

Contribution

It proposes a novel participatory method, CBSD, to incorporate stakeholder perspectives into ML problem formulation, aiming to reduce bias and improve fairness outcomes.

Findings

CBSD enables stakeholder participation in problem formulation.

It helps identify biases early in ML development.

The approach addresses dynamic complexity in high-stakes domains.

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.