论文标题
通过基于社区的系统动态，更公平的机器学习的参与性问题提出
Participatory Problem Formulation for Fairer Machine Learning Through Community Based System Dynamics
论文作者
论文摘要
关于算法公平性的最新研究强调，ML系统开发的问题制定阶段可能是偏见的关键来源，对ML系统公平成果产生了重大影响。但是，很少关注改善ML系统开发的关键阶段公平效率的方法。当前的实践既不说明高风险领域的动态复杂性，也没有说明脆弱利益相关者的观点。在本文中，我们介绍了基于社区的系统动态（CBSD），以此作为一种方法，以使通常排除利益相关者参与ML系统开发过程的问题制定阶段，并促进在这个关键阶段减轻偏见所需的深层问题理解。
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.
