Algorithms increasingly govern societal functions, impacting multiple stakeholders and social groups. How can we design these algorithms to balance varying interests and promote social welfare? As one response to this question, I present WeBuildAI, a social choice–based framework that enables people to collectively build algorithmic policy for their communities. In this talk, I will present a year-long codevelopment process in which we applied the framework. We designed a matching algorithm that operated 412 Food Rescue, an on-demand donation transportation service, by working with their service stakeholders, including donors, volunteers, recipients, and nonprofit employees. I will discuss this work’s implications to algorithmic fairness, accountability, and sensemaking and the potential of participatory mechanisms for algorithmic systems.