WeBuildAI : Participatory Framework for Fair and Efficient Algorithmic Governance

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, we present WeBuildAI, a social-choice based framework that enables people to collectively build algorithmic policy for their communities. The framework consists of three steps: (i) Individual belief elicitation on governing algorithmic policy, (ii) voting-based collective belief aggregation, and (iii) explanation and decision support. We applied this framework to an efficient yet fair matching algorithm that operates an on-demand food donation transportation service. Over the past year, we have worked closely with the service’s stakeholders to design and evaluate the framework through a series of studies and a workshop. We offer insights on belief elicitation methods and show how participation influences perceptions of governing institutions and administrative algorithmic decision-making.