WeBuildAI: Participatory Framework for Algorithmic Governance

Algorithms increasingly govern societal functions, impacting multiple stakeholders and social groups. Howcan we design these algorithms to balance varying interests in a moral, legitimate way? As one answerto this question, we present WeBuildAI, a collective participatory framework that enables people to buildalgorithmic policy for their communities. The key idea of the framework is to enable stakeholders to constructa computational model that represents their views and to have those models vote on their behalf to createalgorithmic policy. As a case study, we applied this framework to a matching algorithm that operates anon-demand food donation transportation service in order to adjudicate equity and efficiency trade-offs.The service’s stakeholders—donors, volunteers, recipient organizations, and nonprofit employees—used theframework to design the algorithm through a series of studies in which we researched their experiences.Our findings suggest that the framework successfully enabled participants to build models that they feltconfident represented their own beliefs. Participatory algorithm design also improved both procedural fairnessand the distributive outcomes of the algorithm, raised participants’ algorithmic awareness, and helpedidentify inconsistencies in human decision-making in the governing organization. Our work demonstrates thefeasibility, potential and challenges of community involvement in algorithm design

Focus: Methods or Design
Source: CSCW 2019
Readability: Expert
Type: PDF Article
Open Source: No
Keywords: participatory algorithm design, collective participation, human-centered AI, matching algorithm, algorithmic fairness
Learn Tags: Basic AI Design/Methods Ethics Fairness Framework
Summary: The WeBuildAI team designed, applied and evaluated this social participatory framework for engaging community stakeholders to enable people to create a decision-making algorithm that fits their needs.